--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1335 reviews by the users' company size, role or industry to find out how Databricks works for a business like yours. aggregate_rating: rating_value: 4.6 review_count: 1335 scale: '5' date_modified: '2026-07-03' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Databricks Reviews **Vendor:** Databricks Inc. **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.6/5.0 **Total Reviews:** 1,335 ## About Databricks Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. ## Databricks Pros & Cons **What users like:** - Users highlight the **seamless integration** of Databricks with AWS, enhancing their data management and processing capabilities. (288 reviews) - Users love the **ease of use** of Databricks, seamlessly integrating with various services for efficient data management. (278 reviews) - Users appreciate the **seamless integration with AWS and Azure** , enhancing collaboration and efficiency in data management. (189 reviews) - Users value the **excellent collaboration** features of Databricks, enhancing teamwork and real-time project alignment. (150 reviews) - Users value the **effective data management features** of Databricks, simplifying their workflows and enhancing decision-making. (150 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (148 reviews) - Users value the **extensive analytical features** of Databricks, facilitating efficient operations and comprehensive data insights. (139 reviews) - Machine Learning (136 reviews) - ML Integration (135 reviews) - Scalability (134 reviews) **What users dislike:** - Users find the **steep learning curve** of Databricks challenging, impacting onboarding and widespread organizational use. (112 reviews) - Users find the **costs for Databricks quite high** , especially when working with large datasets, impacting overall satisfaction. (97 reviews) - Users face a **steep learning curve** with Databricks, making initial adoption and resource management challenging. (96 reviews) - Users express frustration over **missing features** in Databricks, limiting its effectiveness for complex deployments and custom setups. (69 reviews) - Users often face **complexity due to a steep learning curve** and challenges with integration and documentation in Databricks. (64 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (61 reviews) - Performance Issues (57 reviews) - Poor UI Design (53 reviews) - Difficult Learning (51 reviews) - Users experience a **complex setup** process initially, but support helps to simplify the experience over time. (45 reviews) ## Databricks Reviews ### 1. Databricks: Intuitive, Unified Platform with Seamless Integrations and Fast Support **Rating:** 5.0/5.0 stars **Reviewed by:** Sabareeswar K. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** As a data engineer, Databricks has become my go-to platform for end-to-end data work. The ease of use is outstanding notebooks, Delta Live Tables, and Genie all have intuitive interfaces that reduce rampup time significantly. Implementation was smooth thanks to excellent documentation and responsive customer support that actually resolves issues fast. I use it daily, and the sheer number of features from Unity Catalog to AI/BI Genie keeps growing. Integration with cloud storage, BI tools, and ML frameworks is seamless, making it a true unified platform. **What do you dislike about Databricks?** One challenge is the lack of cost transparency at a granular job level it's difficult to pinpoint exactly which pipeline or notebook is driving up DBU consumption without investing in custom monitoring. Auto scaling clusters, while powerful, can silently balloon costs overnight if not carefully configured with proper limits. Additionally, the SQL warehouse tiers can be confusing to choose from upfront, making budget planning tricky for teams. A built in cost allocation dashboard per job or user would be a huge improvement for day to day cost governance. **What problems is Databricks solving and how is that benefiting you?** Databricks has eliminated the silos between our data engineering, analytics, and ML teams. Previously, we juggled multiple tools for ingestion, transformation, and reporting. Now everything lives in one lakehouse. Genie specifically has been a game-changer business stakeholders can ask natural language questions directly against our data without writing SQL, which dramatically reduces ad-hoc request bottlenecks for our engineering team. Decision making is faster, data is more democratized, and we've cut our reporting pipeline overhead by a significant margin. **Official Response from Janelle Glover:** > It's great to hear that Databricks has helped eliminate silos between your data engineering, analytics, and ML teams. We're pleased that Genie has been a game-changer for your business stakeholders. We also understand the challenges you've mentioned regarding cost transparency and auto-scaling clusters. We are continuously working to improve our platform and will take your suggestions into consideration for future enhancements. ### 2. Databricks Makes End-to-End Data Workflows Fast, Collaborative, and Easy **Rating:** 5.0/5.0 stars **Reviewed by:** Karuppusamy V. | Technical Lead, Enterprise (> 1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it simplifies the entire data workflow. Instead of switching between multiple tools for data processing, analysis, and machine learning, everything is available in one place. The notebook environment makes collaboration really smooth it feels natural to work with teammates, share code, and explain logic without extra effort. Another thing I appreciate is the performance. Working with large datasets can usually be painful, but Databricks handles it efficiently in the background. You don’t have to worry much about managing clusters or optimizing everything manually it just works most of the time, which lets you focus more on solving the actual problem rather than dealing with infrastructure. What also stands out is the way it handles data governance and organization. With features like centralized access control and better visibility into data usage, it becomes much easier to manage data responsibly, especially in larger projects. Overall, it gives a good balance between power and ease of use, which is why I enjoy working with it. **What do you dislike about Databricks?** One thing I don’t particularly like about Databricks is that it can get expensive pretty quickly, especially if clusters are not managed properly. If you forget to terminate clusters or run heavy workloads without optimization, costs can spike without much visibility at first. For teams that are still learning or experimenting, this can become a concern. Another downside is that debugging can sometimes feel a bit tricky, particularly when working with distributed jobs. Errors are not always straightforward, and tracing issues across multiple nodes can take extra time compared to working in a simpler local environment. It requires a certain level of experience to quickly understand and fix issues. Also, while the platform is powerful, it has a bit of a learning curve for beginners. Concepts like cluster configuration, job scheduling, and data governance are not always very intuitive at the start. It takes some hands-on time before you feel fully comfortable navigating and using everything efficiently **What problems is Databricks solving and how is that benefiting you?** What Databricks really solves is the problem of handling large-scale data without making the process overly complex. Earlier, working with big data meant dealing with multiple tools, managing infrastructure, and spending a lot of time just setting things up. Databricks simplifies all of that by bringing data engineering, analytics, and machine learning into one place, so the focus shifts more toward solving actual business problems instead of managing systems. It also addresses performance and scalability issues. When working with huge volumes of data, traditional systems often struggle or slow down. Databricks handles this efficiently in the background, allowing workloads to scale without much manual effort. For me, this means I can process large datasets faster and run transformations or queries without constantly worrying about performance tuning. Another big problem it solves is collaboration and data management. In many projects, teams struggle with version control, access management, and keeping data consistent. Databricks makes it easier to collaborate, track changes, and control who can access what. This helps me work more smoothly with others, reduces errors, and ensures that the data I’m using is reliable and well-governed. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a comprehensive and efficient platform for managing data workflows. We understand your concerns about cost management and the learning curve for beginners, and we will share your feedback with our team to further review. ### 3. Databricks Unifies Data, Analytics, and ML for Scalable Lakehouse Workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Harshavarthini G. | Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** Databricks is especially helpful because it brings data engineering, analytics, and machine learning together in a single unified platform, which reduces the need to manage multiple separate tools. Built on Apache Spark, it can process massive datasets quickly and scale smoothly as workloads grow, making it a strong fit for big data use cases. It also supports collaborative notebooks where teams can work together in languages like Python and SQL, which makes it easier for data scientists and engineers to collaborate effectively. With its lakehouse architecture powered by Delta Lake, Databricks combines the flexibility of data lakes with the reliability of data warehouses, helping ensure better data consistency and performance. In addition, it integrates with tools like MLflow to streamline the machine learning lifecycle end to end, from experimentation through deployment. Overall, Databricks simplifies complex data workflows, improves performance, and helps organizations build scalable data and AI solutions more efficiently. **What do you dislike about Databricks?** Databricks does have some limitations, although many of them feel more like trade-offs than outright negatives. A frequently cited drawback is cost: while the platform is flexible and scalable, expenses can rise quickly if clusters aren’t managed carefully. At the same time, that cost often reflects its ability to handle very large workloads efficiently when it’s properly optimized. Another consideration is the learning curve, especially for beginners who aren’t familiar with Apache Spark or distributed systems. That complexity can be challenging at first, but it also comes with the benefit of powerful capabilities once you get comfortable with it. Some users also find that debugging and performance tuning are less straightforward than with simpler tools; however, Databricks offers detailed monitoring and optimization features that can make these tasks easier over time. Finally, because it’s a managed platform, there can be a sense of reduced control compared with fully self-managed systems. In return, it removes much of the operational burden that comes with infrastructure management. Overall, while these areas may be seen as the “least helpful” aspects, they’re often balanced by the platform’s scalability, integration, and productivity gains. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of fragmented data and disconnected workflows across multiple business verticals by providing a unified lakehouse platform. In my role as a data engineer, this allows me to consolidate data from different sources into a single, reliable system using Apache Spark for scalable processing and Delta Lake for ensuring data quality and consistency. This significantly reduces pipeline complexity, improves reliability, and enables faster delivery of clean, governed data to downstream teams. As a result, I’m able to support analytics and machine learning use cases more efficiently while minimizing operational overhead and improving overall productivity across the organization. **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Databricks. It's great to hear that the platform's ability to bring together data engineering, analytics, and machine learning in a single unified platform is benefiting your organization. We understand the trade-offs and challenges you've mentioned, and we're continuously working on these parts of our platform. ### 4. Databricks Streamlines End-to-End ETL with Unity Catalog and AI-Powered Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Dinesh Sundar S. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 30, 2026 **What do you like best about Databricks?** What stands out to me is how Databricks simplifies the end-to-end ETL lifecycle. The platform’s steady integration of new features has noticeably reduced the friction of ingesting data from a wide range of source systems. Unity Catalog (UC) has also been a game-changer for data administration. It offers a centralized, robust governance layer that makes managing complex environments feel much more intuitive and easier to control. I’m especially impressed by the recent AI-driven updates. Genie Code has become an essential part of my workflow; it has dramatically improved my debugging speed and is already proving to be a valuable asset in my current UC migration project. Overall, the way Databricks blends traditional data engineering with assisted intelligence feels genuinely forward-thinking. **What do you dislike about Databricks?** While Auto Loader is powerful, there are still notable gaps in the Lakehouse Data Pipeline (LDP) around schema inference. Right now, when inferSchema is enabled, the inferred schema only applies to the first level of the hierarchy. In complex datasets with multi-nested fields, the lack of deep schema inference creates manual overhead and makes streaming CDC pipelines harder to build and maintain. Lakeflow Connect feels like a step in the right direction, but the library of native connectors still seems incomplete compared to some competitors. And while the AI features (like Genie) are promising and genuinely interesting, they still come across as being in a “developing” stage—sometimes lacking the consistency you need for high-stakes production environments. I’d like to see these capabilities evolve from “innovative extras” into hardened, production-ready tools. **What problems is Databricks solving and how is that benefiting you?** The Problem: Data Silos & Inefficient Support Operations In many organizations, critical institutional knowledge ends up scattered across disconnected systems such as MySQL (structured), Jira (transactional), and Confluence (unstructured). When information is fragmented this way, support teams struggle to find fast, accurate answers for incoming tickets. The result is higher MTTR (Mean Time to Resolution) and a lot of repetitive, manual effort. The Solution: A Unified “Intelligence Platform” Databricks addresses this by serving as a single fabric that connects these silos. In my work, I focus on using the Lakehouse Data Pipeline (LDP) to ingest and unify these different sources into one governed environment. How this benefits my project: I use Databricks for seamless ingestion, centralizing data from MySQL, Jira, and Confluence to build a comprehensive “Knowledge Base” without having to manage multiple, disparate ETL tools. I also rely on native AI integration. With Mosaic AI Vector Search, I can convert the unified data into embeddings directly within the platform, which lets me build an AI Automation Agent for our ticketing system. Finally, it supports automated solutioning. The agent can run vector matching on newly created tickets against the full historical knowledge base and then propose accurate, context-aware solutions to engineers right away. The Impact The biggest benefit for us is operational velocity. Databricks has shifted our data from a passive archive into an active, “intelligent” engine. It reduces time spent on manual research and helps us automate the first line of support, improving the accuracy of ticket resolutions while lowering the burden on our technical teams. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that our platform's end-to-end ETL lifecycle simplification and Unity Catalog have been game-changers for your data administration. We appreciate your feedback on the AI-driven updates and are thrilled to hear that Genie has improved your workflow. We're committed to continuously enhancing our platform to provide a forward-thinking experience for our users. ### 5. Fast, Seamless Databricks for Big Data Pipelines, and Analytics in One Place **Rating:** 4.5/5.0 stars **Reviewed by:** Demetrius A. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** What I love most about Databricks is how fast and connected everything is. Compared to other platforms, it handles heavy big data pipelines without breaking a sweat. But the best part is how easy it is to use that data once it's processed. Whether I need to build a quick analytics dashboard or train custom machine learning models specific to our data, it all connects seamlessly. It just takes the headache out of moving data around and lets you do everything in one place. **What do you dislike about Databricks?** If I had to choose what I dislike, it mainly comes down to the cost and how complex it can be. First, it can get expensive very quickly. If you’re not careful about managing your computing clusters and shutting them down when you’re done, the bills can creep up on you. Second, it can sometimes feel like overkill for simpler tasks. Since it’s built for massive data, having to dig through complicated error logs when something breaks can be a real headache compared to using lighter tools. **What problems is Databricks solving and how is that benefiting you?** The main problem Databricks helps me solve in my business is performance. We used to wait for hours for pipelines to run in ADF, and now we can get them done in minutes. **Official Response from Janelle Glover:** > We're thrilled to hear that you find Databricks fast and seamless for handling big data pipelines and analytics. We understand your concerns about cost and complexity, and we are continuously working to optimize these aspects of our platform to address these challenges. ### 6. Unified ML Platform That Removes Infrastructure Friction **Rating:** 5.0/5.0 stars **Reviewed by:** Hirlekha M. | AI/ ML Technical Lead, Mid-Market (51-1000 emp.) **Reviewed Date:** March 30, 2026 **What do you like best about Databricks?** The unified platform experience is genuinely hard to beat — having MLflow for experiment tracking, Unity Catalog for governance, vector search, and serverless endpoints all in one place removes so much infrastructure friction. Feature engineering pipelines and model deployment feel cohesive rather than stitched together. The SQL warehouse + notebook hybrid workflow also makes it easy to hand off between data engineering and ML work without context switching tools. **What do you dislike about Databricks?** Serverless endpoints have some sharp edges — Spark context initialization behaves differently than in interactive clusters, which can cause silent failures if you're not careful about where you initialize things. Cold start latency on serverless is also noticeable for low-traffic production endpoints. Documentation around some of the newer features (like vector search index configs) tends to lag behind the actual product behavior, so you end up doing a lot of trial and error. **What problems is Databricks solving and how is that benefiting you?** We use Databricks to consolidate ML model development, feature engineering, and deployment for a cards and payments platform — work that previously required juggling separate tools for data processing, training, and serving. The unified environment means our ML engineers can go from raw transaction data to a deployed churn prediction model without leaving the platform. MLflow tracking keeps experiments reproducible, and Unity Catalog gives us the data governance story our banking client needs. It's cut down a significant amount of the coordination overhead that comes with multi-tool ML pipelines. **Official Response from Janelle Glover:** > It's great to hear how Databricks has streamlined your ML workflows and reduced coordination overhead. We appreciate your feedback on the serverless endpoints and documentation, and we'll strive to address these issues to enhance your experience with our platform. ### 7. Lakebase: Powering Data and AI together **Rating:** 5.0/5.0 stars **Reviewed by:** Ajay P. | Manager - Data, AI & Automation, Small-Business (50 or fewer emp.) **Reviewed Date:** September 08, 2024 **What do you like best about Databricks?** I use Lakebase within Databricks as the foundation for our AI solutions, where data models and applications work together seamlessly. Lakebase provides a single unified data foundation to build AI directly on consistent, real-time data. I also like Agent Bricks in Databricks because it helps us quickly build intelligent AI agents and automate workflows using that data. The ease of setup was a significant plus for us, as it was super easy to get started. **What do you dislike about Databricks?** Agentbricks needs more native integration to reduce the manual setup and speed up the workflow automation. **What problems is Databricks solving and how is that benefiting you?** Databricks - Lakebase helps us bring data and AI together on one platform, reducing complexity and avoiding data movements. Agent Bricks allows us to quickly build intelligent AI agents and automate workflows using real-time data. **Official Response from Jess Darnell:** > We're thrilled to hear that you find Lakebase simple to use and packed with features for developing data pipelines and AI. It's great to know that it has been helpful in implementing GenAI and integrating with different sources through LakeFlow. ### 8. From 1 Hour to 10 Minutes: How Databricks Modernized Our Workflow **Rating:** 4.5/5.0 stars **Reviewed by:** Mukundan R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 30, 2026 **What do you like best about Databricks?** We used to use ADF to get data from SQL Server and then work on it in Databricks before putting it into Salesforce. The whole process took a time more than an hour because ADF added extra work. Now everything happens inside Databricks. We transform the raw data in Databricks and put in into Salesforce all in one place. This has made the whole process much faster, it now takes 10 minutes. That is an improvement from what we had with ADF. Delta Lake has also been really useful. It helps us keep track of changes and go back if something goes wrong. We can see what happened before . Fix mistakes easily. Delta Lake also makes sure the data is good before it goes into the pipeline. It stops data from getting in and causing problems later on in Salesforce. This makes the whole process more reliable and easier to take care of. **What do you dislike about Databricks?** Databricks is really good at what it does.. Sometimes it takes a while to get the cluster up and running.. The user interface is slow at sometimes. This can be annoying when we are in a hurry to get things done for Salesforce. The Salesforce connectors in Databricks can be a bit tricky to work with. They often need to be set up right and do not work as we expect. This means we have to put in work when we are trying to figure out problems or keep an eye on the pipelines, in Databricks for Salesforce. **What problems is Databricks solving and how is that benefiting you?** It is solving our performance and reliability issues - by allowing us to extract, transform and load the data into Salesforce all in one place without ADF. This unified workflow has reduced our runtime from 1 hour to 10 minutes giving us faster job completion and on time Salesforce data updates.With delta lake features like ACID transactions and time travel,our data is more accurate and easier to recover when something goes wrong. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been able to significantly improve your workflow by reducing the runtime. It's great to know that Delta Lake has been useful in maintaining data accuracy and providing easier recovery options. We understand your concerns about the cluster setup time and the user interface speed, as well as the challenges with the Salesforce connectors. We appreciate your feedback and will share it with our team for further improvements. ### 9. Databricks: Feature-Rich, User-Friendly, and Keeps Everything in One Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Sivabalan A. | Technical Lead - Data Engineering, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Among the various platforms I’ve worked with, Databricks stands out as a genuinely cohesive environment. It feels less like a bundle of disconnected features and more like a unified workspace—one that can evolve alongside the teams using it. The interface is intuitive enough to lower the barrier to entry, while still delivering the depth and power needed for heavy-duty engineering. One of its biggest strengths is how it consolidates the data lifecycle. By bringing engineering, data science, and SQL analytics under one roof, it helps dissolve the silos that often lead to “data drift” and miscommunication between departments. In practice, it also simplifies the underlying infrastructure, replacing a dozen specialized (and sometimes conflicting) tools with a single, clearer source of truth. Beyond simply “keeping things clean,” the platform also shines when it comes to collaborative transparency. With notebooks and experiments shared in real time, the gap between an initial data idea and a production-ready model can be dramatically shortened. On top of that, its commitment to open standards like Delta Lake means you’re not boxed into a proprietary black box—you’re building on a foundation that aligns with the broader data community’s direction. Overall, it strikes a rare balance: a polished, user-friendly wrapper around some of the most powerful distributed computing engines available today. **What do you dislike about Databricks?** The “Big Task” Breakdown When Genie processes a large volume of data, it often ends up sending a huge amount of JSON back to the browser so it can render those tables and visualizations. Memory overload: Browsers (and especially Chrome) can be real memory hogs. If a Genie response includes a very large result set or a massive execution plan, RAM usage can spike quickly, which can lead to that familiar “Not Responding” hang. The “DOM” lag: Every row in a table and every line of code becomes an element the browser has to keep track of. As you scroll or type, the browser has to repaint thousands of these elements. When the task is too large, the browser’s main thread can get tied up rendering, and your typing starts to feel like it’s trailing behind by a few seconds. **What problems is Databricks solving and how is that benefiting you?** You’ve nailed the core reason Databricks is winning over so many data teams: they’re reducing the “integration tax.” In most companies, you can easily lose around 30% of your time just moving data between the “storage” tool, the “processing” tool, and the “BI” tool. The AI/BI Dashboard is a great example of this broader shift—from a “collection of tools” to a more unified platform. What began as a basic visualization layer has evolved into a “Compound AI” system. Here’s how it has become so useful: The “Ask Genie” integration: You’re no longer limited to staring at a static chart. As of 2026, every published dashboard includes an “Ask Genie” button by default. If a stakeholder notices a spike in a line chart, they don’t have to call you; they can right-click the chart and ask, “Genie, why did this drop on Tuesday?” and it will use Agent mode to track down the driver. Direct-to-warehouse speed: Because it lives inside Databricks, there’s no need to “extract” data to a separate BI server. It queries the data where it already lives (Unity Catalog), which means the dashboard stays as fresh as your last ETL run. AI-assisted authoring: You can build entire widgets just by describing what you want. Instead of dragging fields around, you can type, “Show me a funnel chart of our sales conversion by region,” and it generates the SQL and the visualization for you. Deep governance: Since it’s built in, your security policies (row-level security, tags) follow the data automatically. You don’t have to recreate permissions in a separate tool like Tableau or Power BI. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of Databricks in reducing the 'integration tax' and streamlining data movement between storage, processing, and BI tools. We're pleased to hear how the AI/BI Dashboard and Genie have been valuable in providing direct-to-warehouse speed and AI-assisted authoring. ### 10. Databricks Genie Code - Agentic Applied AI for end-end SDL liefecycle **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Senior Cloud Solution Architect - Accenture Data & AI (Applied Intelligence), Enterprise (> 1000 emp.) **Reviewed Date:** October 03, 2023 **What do you like best about Databricks?** Genie Code 1) Genie Code automated our ETL processes, reducing manual effort and increasing efficiency. With Agentic’s SDL, we implemented CI/CD pipelines for faster, seamless updates and deployments. 2) Genie Code streamlined complex STTM mappings, improving accuracy and speed. Agentic’s real-time updates ensured mapping adjustments were made dynamically to align with changing transaction data. 3) We defined automated unit tests using SKILL.md, ensuring data transformations are validated before deployment. This reduced errors and ensured data quality, boosting confidence in our analytics. 4) Using Skills.md, we added custom extensions to Genie Code, such as integrating third-party data for enriched reports. This agility allowed us to quickly adapt to business needs and deliver new capabilities. 5) Agentic’s SDL enabled real-time data processing, providing immediate analytics for decision-making. Our marketing and sales teams now act on fresh data instantly, improving response times and overall efficiency. **What do you dislike about Databricks?** Hope it can be improved in next update - Debugging issues in complex workflows can be time-consuming due to limited visibility into intermediate data transformations. Genie Code lacks advanced error recovery mechanisms, making it difficult to manage failures in large-scale data pipelines. As data volume increases, Genie Code’s performance can degrade, requiring significant manual adjustments to ensure smooth operation at scale. **What problems is Databricks solving and how is that benefiting you?** 1) Scalable Processing - Built on Databricks' Spark-based architecture, Genie Code efficiently handles and scales processing for massive datasets, ensuring performance even with increasing data volumes. 2) Genie Code automates end-to-end ETL workflows, from data extraction to transformation and loading, streamlining data operations and eliminating manual tasks. 3) Real time collaboration - Genie Code enables real-time collaboration across teams by using shared notebooks, making it easier for data professionals to build and refine workflows collectively. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks Data Intelligence Platform is helping you with unified lakehouse platform, workflow orchestration, integrations, and data sharing. We are committed to providing solutions that meet your business needs. ### 11. Databricks’ Unified Platform: Fast SQL, Streamlined Pipelines, and Context-Aware AI **Rating:** 5.0/5.0 stars **Reviewed by:** Deeraj R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** The unified platform experience is what keeps me on Databricks. Having notebooks, pipelines, SQL warehouses, ML, and governance all in one place under Unity Catalog means I’m not constantly stitching together five different tools just to get work done. Lakeflow Pipelines (formerly DLT) makes it straightforward to build medallion-architecture pipelines, and the Photon engine delivers real performance gains on SQL workloads without requiring any code changes. Recent additions like Genie Code and background agents also show they’re serious about agentic AI—it doesn’t feel like a bolt-on copilot, because it can actually understand your data context through Unity Catalog. Serverless compute has been another big quality-of-life improvement as well, since I no longer have to wait for cluster spin-up when I just want to run quick, ad hoc queries. **What do you dislike about Databricks?** Cost management can be tricky—DBUs add up quickly if you’re not careful with cluster sizing and warehouse auto-scaling. The pricing model also isn’t always transparent, especially when you’re mixing serverless and classic compute. Unity Catalog is powerful, but the initial setup and the migration from legacy HMS can be painful, particularly for large orgs with years of existing Hive metastore objects. The documentation is generally good, yet it sometimes lags behind new feature releases. On top of that, the workspace UI can feel sluggish at times, especially when you’re working with a large number of assets. **What problems is Databricks solving and how is that benefiting you?** Before Databricks, our data stack was fragmented — separate tools for ETL, analytics, ML, and governance. That meant constant context-switching, duplicated data, and governance gaps. Databricks consolidates all of that into one lakehouse platform. Delta Lake gives us reliable ACID transactions on the data lake, Unity Catalog handles lineage and access control across the board, and SQL warehouses let our analysts self-serve without needing a separate data warehouse product. It's cut our pipeline development time significantly and made data governance something we can actually enforce consistently instead of hoping for the best. **Official Response from Janelle Glover:** > We're glad to hear that you're enjoying the unified platform experience and finding value in Lakeflow Pipelines, Photon engine, and Genie Code. We understand your concerns about cost management and transparency in pricing, as well as the challenges with initial setup and workspace UI. Your feedback is valuable and will be shared with our team for further improvements. ### 12. All-in-One Databricks Platform with Strong Governance, Fast Spark Performance, and Genie **Rating:** 5.0/5.0 stars **Reviewed by:** Sabareeswara S. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** The all-in-one platform eliminates tool sprawl. Unity Catalog gives you governance, lineage, and discoverability without bolting on a separate catalog. The notebook UI is clean and makes iterating on PySpark fast. Genie is the standout AI feature it turns curated tables into natural language interfaces for business users, and the SDK lets you configure it programmatically so it stays maintainable. DLT handles pipeline orchestration well. Performance on Spark workloads is solid, especially with Photon. Integrations with Airflow, S3, and the broader ecosystem are straightforward. For the ROI, consolidating what used to require multiple tools into one platform pays for itself in reduced complexity. **What do you dislike about Databricks?** Pricing can be hard to predict. Compute costs scale quickly if you're not careful with cluster sizing and SKU selection, and it's not always obvious which workload tier you actually need until you see the bill. The notebook IDE, while functional, still lags behind a real editor for refactoring, multi-file navigation, and code review workflows **What problems is Databricks solving and how is that benefiting you?** Tool consolidation is the biggest one. Before, you'd need separate systems for ingestion, transformation, warehousing, governance, and serving each with its own learning curve, maintenance overhead, and integration headaches. Databricks collapses that into a single platform. Unity Catalog solves the data governance problem by giving you lineage, access control, and discoverability in one place instead of managing permissions across disconnected systems. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that you find Delta Live Tables, Unity Catalog, and Genie beneficial. We also appreciate your feedback on the pricing and will share your comments with our team. ### 13. Databricks Keeps Removing Friction with Strong Governance and Intuitive AI Tools **Rating:** 4.5/5.0 stars **Reviewed by:** Praveenkumar S. | Solutions Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** What I like most about Databricks is how its features have consistently matched the evolving needs of engineering teams. Over the years, I’ve seen it grow from a solid data platform into a workspace that genuinely streamlines how we build and manage data and AI solutions. Unity Catalog has been one of the biggest improvements for us having a single place to manage permissions and lineage has removed a lot of manual steps we used to handle separately across systems. Genie AI and BI have also become part of my regular workflow; being able to generate SQL or explore datasets through natural conversations helps teams get to answers faster, especially when we’re under time pressure. The Apps capability has added unexpected value by letting us create and share simplified internal tools directly within the platform, eliminating the need to stand up extra infrastructure. And with Lakebase, we’ve been able to support more transactional-style use cases without losing the flexibility of a lake, which has made certain pipelines far easier to maintain. Altogether, these improvements have removed a lot of friction from day‑to‑day work and made the platform something I genuinely enjoy using as it continues to evolve. **What do you dislike about Databricks?** What I dislike about Databricks is that some of the newer AI experiences especially Genie for code generation can feel unstable at times and may lose context during longer development sessions. It disrupts my workflow when the assistant can’t retain earlier logic or maintain continuity across multiple iterations. I’ve also noticed a gap in native connectors for certain enterprise systems like DFS, SMB shares or windows-based source systems, and platforms such as DB2 on AS/400, which many customers still rely on. Even though Databricks continues to expand its ecosystem, the lack of direct connectivity in these areas often means we need extra middleware or custom pipelines to bridge the gap. None of these are deal-breakers, but they’re areas where the platform’s otherwise smooth experience can still feel a bit incomplete. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us address several long‑standing challenges in how we manage and deliver data and AI. Before adopting its newer capabilities, we were dealing with fragmented governance, duplicate datasets, and a lot of manual effort to keep permissions and lineage consistent across different systems. Unity Catalog improved this by giving us a single place to manage security and ownership, which reduced confusion across teams and noticeably cut down on rework during audits. We also used to spend a significant amount of time helping teams explore data or draft queries. With Genie AI and BI, they can now generate SQL, summaries, and visual insights more independently. As a result, the time from a question to a usable answer has shortened, especially when we’re working under tight delivery cycles. Another pain point was building small internal tools around our data. Setting up separate infrastructure or hosting environments created unnecessary overhead. With Databricks Apps, we can now build and share these tools within the platform itself, which saves setup time and reduces ongoing maintenance. Finally, we struggled to support workloads that needed both the flexibility of a lake and the reliability of a database. Lakebase helped close that gap by enabling transactional‑style operations directly on our lake data, which simplified several pipelines and reduced the number of systems we have to maintain. Overall, Databricks has moved us from juggling multiple disconnected tools to working in a more unified and predictable environment. That shift has sped up delivery, lowered operational overhead, and improved the clarity of our workflows. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear that our platform has been able to consistently meet the evolving needs of your engineering teams and streamline your data and AI solutions. We appreciate your feedback on Unity Catalog, Genie, and Lakebase, and we're committed to continually improving and evolving our platform to provide a smooth and enjoyable user experience. ### 14. Fast, Governed Self-Service Data Exploration with Databricks Genie **Rating:** 3.5/5.0 stars **Reviewed by:** Yuvashree M. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** As a data engineer, I use Databricks Genie to interact with data in natural language, while still relying on the same governed tables, metrics, and semantic models that my team has built. Instead of jumping straight into SQL notebooks for every exploratory ask, I or business users can phrase questions in plain language and let Genie translate them into structured, catalog‑aware queries. This keeps self‑service fast but also secure and governed. **What do you dislike about Databricks?** Laptop stability when multitasking My laptop can hang or become noticeably sluggish when I’m working with multiple Genie tabs and dashboards at the same time, especially during heavier queries or more demanding visualizations. This hurts the overall user experience and can slow down iterative development and analysis. Latency with complex data models With very wide schemas or more complex semantic models, Genie sometimes selects suboptimal joins or an overly broad/narrow level of granularity. As a result, I still need to review the generated SQL and optimize it myself. In that sense, it remains a helpful assistant rather than a fully autonomous query engine. **What problems is Databricks solving and how is that benefiting you?** In a recent project, the business wanted to understand a decline in customer‑lifetime‑value (CLV) in a specific region. A product manager used Genie to explore CLV trends by region and cohort, excluding refunds, directly from an AI/BI dashboard. From that conversation, I captured the core logic, wrapped it into a Delta Live Table pipeline, and scheduled it as a recurring job. This reduced ad‑hoc requests by roughly 30–40% and enabled ongoing self‑serve access to CLV insights while I focused on tuning performance and data‑quality rules. Overall, Genie helps me talk with my data in natural language, improves how quickly we uncover insights, and supports better data‑quality practices—though working across many Genie‑backed tabs can strain local hardware and sometimes slow down the workflow. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with using Genie for self-service data exploration. We apologize for the issues you've noticed with stability and latency. Our team is actively working to address these concerns and enhance the user experience. ### 15. Databricks Genie Nails Unity Catalog Migrations with Context-Aware Guidance **Rating:** 4.0/5.0 stars **Reviewed by:** Nandhini E. | Senior Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks Genie's contextual understanding of Unity Catalog is genuinely impressive. While working through a complex UC migration, navigating three-level namespaces, volume paths, security modes, and widget-driven SQL execution, Genie reasoned through the specifics instead of falling back on generic answers. It really speaks the UC migration language, which cuts down on a lot of back-and-forth and makes troubleshooting feel more direct. Overall, the platform is powerful for managing large-scale data engineering work across Python, Scala, and notebook-based pipelines, all in one place. **What do you dislike about Databricks?** My biggest frustration with Genie is the lack of persistent session memory. On a long-running migration project with 60+ test cases and multiple interconnected components, having to re-establish context every session creates real overhead. Genie also struggles with cross-component reasoning: it handles individual notebooks well, but tracing issues across multiple layers of a framework is still largely a manual effort. Occasionally, the responses feel overly cautious when what’s needed is a more direct, confident answer. **What problems is Databricks solving and how is that benefiting you?** We’re using Databricks to carry out a full Unity Catalog migration for a large, automated ingestion framework, moving off the legacy Hive Metastore while also upgrading the runtime environment. Databricks provides a unified platform where the migration work, testing, and validation can all happen in one place. During testing, Genie in particular helped speed up root-cause analysis, for example, it pinpointed why a data extraction notebook was failing to resolve UC-managed table references and identified that adding a USE CATALOG statement was the fix. That kind of targeted, context-aware assistance directly reduces investigation time during complex migrations. **Official Response from Janelle Glover:** > It's fantastic to hear that Databricks is helping to streamline your Unity Catalog migration and testing processes. We appreciate your specific example of how Genie's context-aware assistance has directly reduced investigation time during complex migrations. We also appreciate your feedback on efficiency and will take your comments into consideration for future improvements. ### 16. Driving AI and Data Innovation with a Unified Databricks Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Ajay Kumar P. | Associate Consultant-Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 09, 2023 **What do you like best about Databricks?** I use Databricks for ETL, Reporting, and AI, and I appreciate that it works as one unified solution for all data and AI needs. It makes it easier to track data and create insights, helping us deal with data silos. I like the Unity Catalog as it helps us manage and govern data in one place. I also like using AgentBricks as a multi-agent system for creating AI applications from PDFs and other documents. I find Genie valuable as it allows business users to ask questions in natural language and get exact answers. The initial setup of Databricks was very easy, making the transition smooth. **What do you dislike about Databricks?** I think workflow could be improved by adding multiple triggers to the same pipeline, as for now, if we want to schedule the same pipeline multiple times in a day, we have to clone it for each time. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to eliminate data silos and make data tracking and insights creation easy. Unity Catalog manages data governance, AgentBricks develops AI applications, and Genie provides answers using natural language on structured data. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks Intelligence Platform is providing value by addressing data governance issues and streamlining data management. Your feedback on the need for more robust workflows is noted, and we are committed to continuously improving our platform to better meet the needs of Data Engineers, ML Engineers, and Analysts. ### 17. Genie Code Agent Mode Made Our Migration to Databricks Fast and Accurate **Rating:** 4.0/5.0 stars **Reviewed by:** Dharun T. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 26, 2026 **What do you like best about Databricks?** Genie Code (Databricks Assistant Agent) — I’m currently working on migrating existing workloads from ADF and SQLMI to Databricks. As part of that, I need to convert stored procedures and ADF dataflows into Databricks notebooks. Initially, we refactored all the code manually, but once Agent Mode was available in preview, we tried using it to convert the stored procedures and dataflows into Databricks PySpark code. I was impressed by the accuracy: it handled about 90% of the code conversion without errors, aside from some case-handling and similar adjustments. Also, Lakeflow Connect helped me connect SharePoint and SFTP data to Databricks more easily. **What do you dislike about Databricks?** It’s not a major issue, but in my project the client asked us to generate table and column descriptions using AI in Unity Catalog. For each environment, these descriptions vary, and I have around 300 tables just in the Bronze zone. Having to click into each table and generate AI descriptions one by one is very time-consuming, and the results are not consistent across environments. It would be much more efficient if we had an option to generate descriptions at the schema level, and if there were an information schema or system tables that stored table and column descriptions as metadata. That way, we could easily replicate them across environments. In some cases, clients also have source system documentation we could leverage to generate more accurate table and column descriptions. **What problems is Databricks solving and how is that benefiting you?** One of my main scenarios was migrating all the existing stored procedures and ADF dataflows into Databricks notebooks. Doing this manually took more than 6 hours to complete both the development and the validation. Later, we used Agent Mode Preview and converted over 80+ medium/complex stored procedures and 20+ ADF dataflows into Databricks notebooks. This saved more than 100+ hours, and it also generated validation scripts for each table to close out unit testing. Apart from the Agent Assistant, we also used external volume. Previously, we relied on the Azure library for file processing in ADLS storage, but we ran into rate-limit issues, couldn’t process in parallel, and sometimes the job would abort. After we created an external volume pointing to the required ADLS container, we achieved parallel processing and faster reads and writes, instead of using custom Python code. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Genie and Lakeflow Connect in Databricks! We're glad to hear that it has made your migration process fast and accurate. ### 18. Databricks: A True Unified Analytics & AI Platform That Boosts Speed and Reliability **Rating:** 5.0/5.0 stars **Reviewed by:** Amit D. | Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 26, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it finally delivered what every data engineer/data professional has been wishing for — a true unified analytics and AI platform. I remember working across five different tools just to get a single pipeline from ingestion to reporting. Databricks collapsed all of that into one environment, and that changed everything for me. Delta Lake was the first breakthrough. When it arrived around 2020, ACID transactions and time‑travel immediately eliminated the operational pain we used to consider “normal.” If a job corrupted a table, I could roll back to a previous version in seconds instead of spending hours restoring backups. That reliability alone saved multiple downstream failures. Before Delta existed, our pipelines relied heavily on overwrite patterns because there was no reliable way to apply updates or handle late‑arriving data safely. Overwrites were slow, expensive, and risky — especially for large tables. A single failure during overwrite could leave the table in a half-written, inconsistent state. Processing took longer, compute costs shot up, and recovery often meant manually rebuilding partitions from scratch. The ROI became obvious as soon as we used Databricks end‑to‑end. Because one platform handles ingestion → transformation → ML → BI → governance, we retired entire categories of legacy tools and reduced operational overhead dramatically. Then Genie arrived — and it genuinely transformed my day‑to‑day work. I once needed a PySpark module for data quality checks. Genie generated the full logic — null checks, schema validation, aggregations — in seconds. Instead of spending 30 minutes writing boilerplate, I spent 3 minutes refining the logic. It shifted my focus from syntax to decisions. Integrations are another strength. Connecting Databricks to S3, SQL Server, and especially Power BI has been seamless. Publishing Delta tables directly to BI models removed the need for brittle extracts and sped up refreshes. Unity Catalog made everything even cleaner with consistent permissions and lineage. Performance is consistently strong when it matters — heavy joins, window functions, multi‑stage pipelines, or streaming workloads. Serverless compute starts instantly, and workloads scale predictably even under pressure. Finally, onboarding surprised me. Features like serverless compute, natural‑language queries, AI‑generated code suggestions, and automatic comments make Databricks intuitive even for engineers new to Spark. It feels like the platform actively helps you learn. In short: Databricks lets me work faster, recover instantly, integrate seamlessly, and scale confidently — all in one place. It’s the rare platform that improves both speed and reliability at the same time. **What do you dislike about Databricks?** What I dislike most about Databricks is the cost visibility and predictability. Even as an experienced engineer, it can be difficult to get a straight, real‑time view of what a workflow will cost before running it. Photon vs. standard runtime, autoscaling behaviour, shuffle-heavy operations, DBUs—these can stack up quickly, and cost surprises happen unless you actively monitor and tune everything. A simple pipeline misconfiguration can quietly double your spend. Another challenge is the rapid pace of new features and changes. Databricks innovates incredibly fast, which is great, but it also means features may land before documentation, best practices, or governance patterns are fully mature. Sometimes functionality behaves differently across runtimes or cloud providers, and staying on top of everything requires continuous learning and refactoring. This can create team friction and technical debt. In short: Databricks is exceptional, but the cost model isn’t always transparent, and the rapid feature rollout can introduce operational complexity that teams must actively manage. **What problems is Databricks solving and how is that benefiting you?** Business : Before adopting Databricks, our aerospace analytics environment — particularly around Customer engine health monitoring — suffered from the same challenges many traditional engineering organisations face. We had multiple disconnected systems handling telemetry ingestion, fault-code processing, fleet analytics, and maintenance prediction. Data from engine sensors (FADEC, vibration, thermals, oil systems) arrived in different formats and needed heavy manual work just to normalise. Pipelines relied on full overwrites because our legacy setup didn’t support updates or late-arriving data, which made processing slow and expensive. We struggled with slow ingestion of engine telemetry, inconsistent datasets across engineering teams, and long turnaround times for anomaly detection models. Architecture challenge: Before using Databricks, we were operating in a fragmented data landscape. We had multiple systems, disconnected storage layers, and a heavy reliance on overwrite‑based ETL jobs because our old data platform couldn’t support updates, late‑arriving data, or ACID guarantees. This meant pipelines were slow, error‑prone, and expensive. Rolling back bad data could take hours, and data inconsistencies across teams were common. We struggled with siloed systems, slow pipelines, unreliable data, and high operational cost. We struggled with manual overwrites and inconsistent data — but now we can use Delta Lake with ACID and time‑travel, which has resulted in: Instant rollback from data corruption scenarios Reliable incremental processing instead of full overwrites Consistent data consumed across engineering, BI, and ML teams This reduced our telemetry pipeline processing window from hours to under 30 minutes for a fleet‑wide daily batch.. We struggled with multiple tools and duplicated architectures — but now we have one unified Lakehouse, which has resulted in: A single platform for ingestion → transformation → ML → BI → governance Removal of 3–5 legacy tools (ETL schedulers, BI extracts, legacy ML infra) Lower maintenance and licensing overhead We struggled with slow development cycles — but now we can leverage Genie for AI‑assisted engineering, which has resulted in: 70–80% faster creation of PySpark modules Automatic generation of schema checks, null checks, and DQ logic More time spent on decisions, less on boilerplate code For example, a data quality module that used to take 30 minutes now takes 2–3 minutes to scaffold. We struggled with inconsistent governance — but now Unity Catalog gives us end‑to‑end visibility, which has resulted in: Faster onboarding (reduced from days to minutes) Centralised permissions, lineage, and audit trails Stronger compliance alignment We struggled to scale pipelines and ML workloads — but now we use distributed compute + Photon, which has resulted in: Large joins and window operations executing up to 10× faster Stable handling of terabyte‑scale datasets Predictable performance even under heavy workloads **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear how our platform has improved your workflow and provided reliability and speed. We appreciate your feedback and are committed to continuously enhancing our platform to better serve your needs. ### 19. Streamlined Collaboration and Predictive Insights with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** Rohan M. | Software Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** June 09, 2026 **What do you like best about Databricks?** I appreciate how Databricks helps our different teams to collaborate together. A shared notebook project allows our marketing analysts, supply chain engineer, and data scientist to work together in real-time. The version history within Databricks prevents any confusion about the latest developments. I like that it helped us build predictive models to forecast demand more accurately, and we created visual dashboards that we shared with our leadership team, giving them clear regional insights. Within a few months, we saw measurable lifts in sales and improved profit margins. Databricks streamlined processes that used to take weeks of manual spreadsheets and emails, now happening in days with fresh and reliable data. The cross-departmental data sourcing it provides breaks down silos in our organization, enabling smarter and faster decision-making based on a complete picture rather than fragmented departmental views. The setup was straightforward right on the cloud, avoiding messy offline setups. **What do you dislike about Databricks?** There were delays in obtaining permissions for enterprise security and approval workflows while sharing data. **What problems is Databricks solving and how is that benefiting you?** Databricks enables real-time collaboration across teams, fosters holistic insights by breaking down departmental silos, and speeds up processes that used to take weeks. It helps in building predictive models, sharing clear visual insights, and making smarter decisions with reliable data. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has helped your teams collaborate in real-time and improve decision-making with predictive insights and visual dashboards. It's great to hear about the measurable lifts in sales and improved profit margins. We're continuously working to improve our permission and security workflows to provide a smoother experience for our enterprise users. ### 20. A Unified Platform for Scalable Data & AI Workloads **Rating:** 4.5/5.0 stars **Reviewed by:** Janani D. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks is great because it brings everything you need for data and AI into one place. Instead of switching between different tools for data engineering, data cleaning, analytics, and machine learning, you can do it all in a single environment. That makes life a lot easier. **What do you dislike about Databricks?** Databricks is not beginner-friendly. You often need solid data engineering skills to use it effectively. Reviews point out that while Databricks is extremely capable, it’s “a high‑end workshop” that requires expertise and is not easy for less technical teams.Databricks uses cost units (DBUs), which many people find difficult to estimate and manage. Even expert reviews highlight that its pricing is famously complicated and can hide unexpected costs. **What problems is Databricks solving and how is that benefiting you?** Databricks uses the Lakehouse architecture to combine the strengths of data lakes and data warehouses into one unified platform. This means structured and unstructured data live together and are ready for analytics or machine learning. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks' architecture is benefiting you. We designed our platform to address the challenges of managing structured and unstructured data, and it's great to hear that it's making a positive impact on your analytics and machine learning workflows. ### 21. Seamless Data Visualization and Storage with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** Siddharth V. | Data Science and Product Analytics, Mid-Market (51-1000 emp.) **Reviewed Date:** June 07, 2026 **What do you like best about Databricks?** I really love that Databricks has a UI that is essentially very simple to understand, and the categorizations of data make it easy to find and manage repositories. It's also very easy to set up jobs right on the fly without writing extensive scripts, which is a really good functionality. The native visualizations on Databricks allow me to uncover a lot of insights and make business-driven decisions. Additionally, the role-based access is very seamless, and the functionality provided by Databricks makes it very valuable. The native notebooks feature is also very, very valuable. Overall, with the amount of functionality it has, using Databricks is a buy. **What do you dislike about Databricks?** Maybe the multi-select cursor functionalities, which I initially had in open source Redash, might be very useful for productivity. It's a minor kind of functionality. Other than that, Databricks is really useful, and not much changes which I would recommend. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for usage analytics, understanding data storage, uncovering insights, visualizing data, and making business-driven decisions. **Official Response from Jess Darnell:** > Thank you for your feedback! We're happy to hear that you find Databricks' functionality valuable for uncovering insights and making business-driven decisions. We'll take note of your suggestion regarding the multi-select cursor functionalities for future improvements. ### 22. Very powerful tool with Spark and big data. **Rating:** 4.5/5.0 stars **Reviewed by:** Krupa P. | Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 04, 2026 **What do you like best about Databricks?** In fact, the most valuable thing about Databricks is that you do not require worrying about looking after the Spark infrastructure. Previously, it took us so much time to configure clusters manually and here, in a few clicks, you can spin up a cluster. The collaborative notebooks are also very much helpful. My teammates and I are able to collaborate in the same notebook and write Python or Scala or SQL in the same location and share the output in a short time. The connection to AWS and Git is also very fluid, and thus pushing code to production is not demanding a lot of effort at the moment. **What do you dislike about Databricks?** The most significant issue of mine is the cluster start time. There are also cases that I would simply need to make a minor change in the code and the cluster can take about 5-7 minutes to spin up a cold start. It actually disrupts the development. **What problems is Databricks solving and how is that benefiting you?** We are deploying Databricks to create our ETL pipelines and process large volumes of customer data every day. Our local machines would crash prior to using Databricks due to memory problems with large datasets. At this point, we simply push it all to the Databricks cloud. It has completely addressed our scaling problems. Also, job scheduling is extremely simple here, even simple daily pipelines do not require orchestrators such as Airflow. It saves us much time in maintenance. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks to be a powerful tool for managing Spark and big data. The ease of spinning up clusters and the collaborative notebooks are indeed some of the key features that our users appreciate. ### 23. This is very powerful for big data and machine learning but watch the cluster costs! **Rating:** 5.0/5.0 stars **Reviewed by:** Aruna P. | Senior IT Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** The best thing is that we don't have to do any infrastructure to manage now. My team was spending too much time on setting up Apache Spark cluster, managing yarn, and memory crashes on-premise before. With Databricks, we could – within 2-3 clicks – spin up a cluster; collaborative notebooks are very nice! Data engineers and data scientists share the same notebook, so they can collaborate on the same notebook, at the same time. We can have Python, Scala and SQL together in one place without changing any environments. Another super solid feature is delta lake; those provide us with transactions over raw data, this saved us from a lot of data corruption issues since in the past. **What do you dislike about Databricks?** Frankly, its cost is quite high. They charge for DBUs (Databricks Units) and then the cloud provider charge (we're using AWS). Unless you are monitoring, the bill will shoot like a rocket. At times, my developers forget to shutdown the clusters and, if auto-termination is not configured correctly, it's running all night and we get an interest big wake up call in the billing portal the next morning. **What problems is Databricks solving and how is that benefiting you?** We had our data in a lot of different places prior to Databricks. The marketing data was somewhere else, transactional database was somewhere else. We may have had a lot of problems with silo thinking. We are porting Databricks to implement our Lakehouse architecture.We are deploying Databricks to create our Lakehouse Architecture. Now, all raw data will be transferred to S3 and will be cleaned, processed and BI Reporting will be done on Databricks. Went a long way to help resolve our speed issue. Our daily ETLs used to run from 6 - 8 hours. Today, the same pipelines are completing within 45 minutes or less, thanks to Spark optimization in Databricks. The reporting of my business team is providing on time in the morning; thus, the decision making is very fast. **Official Response from Aunalisa Arellano:** > We appreciate your feedback on the benefits of using Databricks for your data management and processing needs. It's great to hear that it has helped to centralize your data and improve processing speed. We understand your concerns about the cost and the potential for unexpected billing spikes. We recommend closely monitoring cluster usage and considering auto-termination to help manage costs. Please feel free to reach out directly to your account team for further questions or feedback. ### 24. Databricks Notebooks Make Collaboration Seamless Across Python, SQL, and Scala **Rating:** 5.0/5.0 stars **Reviewed by:** Joseph F. | Cloud Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 25, 2026 **What do you like best about Databricks?** Databricks collaborative notebooks are really useful and let me work in whatever language I need to meet my requirements effectively. The ability to mix Python, SQL and even Scala within a dashboard makes collaboration and teamwork much smoothet. I also appreciate how easily it integrates with other tools and cloud platforms, so it fits into my existing workflows without very little friction. **What do you dislike about Databricks?** I like their customer support and the frequent updates are a big reason this has become my favorite for data management, I also appreciate how well it integrates with external tools like Power BI for reporting its really good. **What problems is Databricks solving and how is that benefiting you?** Its simplifies cross team collaboration and helps us work through large datasets without having to worry too much about infrastructure or analytics overhead. Calcuations and reporting are fast, which has improved our development cycles and reduced the back and forth between the engineering and analytics teams. **Official Response from Janelle Glover:** > It's great to hear that Databricks is simplifying cross-team collaboration and improving development cycles for you. We strive to provide a platform that reduces infrastructure and analytics overhead, allowing teams to focus on their core objectives. ### 25. Eliminates the fragmentation tax for ML teams, but Unity Catalog migration takes patience **Rating:** 5.0/5.0 stars **Reviewed by:** Sachin G. | Machine Learning Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** Managing end-to-end machine learning pipelines, specifically training and deploying multi-agent models and recommendation engines.What I appreciate most about Databricks is how it completely eliminates the coordination overhead—the fragmentation tax—between our data engineering and data science teams. Before Databricks, we were losing hours every day moving data between unmanaged data lakes, proprietary data warehouses, and our isolated machine learning compute clusters. Having MLflow natively managed inside the Databricks workspace is a massive advantage for my day-to-day workflow. I no longer have to worry about setting up tracking servers or maintaining infrastructure just to log my training metrics, because Databricks handles the automatic updates and maintenance seamlessly. Every experiment is automatically tracked, and the model registry seamlessly handles version control, making the handoff from experimentation to production deployment incredibly smooth. Additionally, the recent updates to MLflow for evaluating GenAI agents, specifically the ability to use trace-derived baselines to generate runnable evaluation scripts, have saved me countless hours of manual assembly. **What do you dislike about Databricks?** The transition to Unity Catalog has been a significant hurdle for our team. Upgrading our legacy workspace to support Unity Catalog's centralized access control and lineage tracking involved a steep learning curve, especially when dealing with privilege inheritance and ensuring the correct schema privileges were granted across the board. Furthermore, while the platform beautifully abstracts away a lot of DevOps work, it can obscure underlying infrastructure costs. It is far too easy for an engineer to spin up an oversized compute cluster for a simple exploratory data analysis task, leading to sudden and severe spikes in our monthly cloud bill. You have to be extremely disciplined with setting strict auto-termination policies and cluster management rules to keep costs in check. The user interface can also feel a bit tedious at times, requiring you to click through multiple layers in the Catalog Explorer just to view the model details page and trace table-to-model lineage. **What problems is Databricks solving and how is that benefiting you?** The primary problem Databricks solved for us was the massive bottleneck in deploying machine learning models to production. We used to struggle with the classic issue where a model worked perfectly in a local notebook but failed in production due to environment mismatches and a lack of proper version control. By standardizing on Databricks and the managed MLflow environment, we established a strict, documented approval chain that satisfies both our engineering standards and our strict compliance requirements. A real-life example of this was when we recently deployed a multi-agent system for customer churn prevention. We were able to run the inference, monitor the agent's safety and relevance metrics using MLflow's built-in judges, and continuously track the outputs all in one unified platform. This consolidated architecture cut our deployment timelines drastically and significantly reduced the time we spent debugging production errors. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on your experience with Databricks. We are thrilled to hear that you are enjoying the benefits of managing end-to-end machine learning pipelines seamlessly. We understand that the transition to Unity Catalog has presented challenges for your team, and we appreciate your patience as you navigate this process. Your insights on infrastructure costs and user interface are valuable, and we will share this feedback with our team for further improvement. We are committed to providing a platform that streamlines your workflows and enhances productivity. If you have any specific concerns or need assistance with the Unity Catalog migration or any other aspect of Databricks, please feel free to reach out to us. We are here to support you every step of the way. Thank you for choosing Databricks! ### 26. Phenomenal Spark Performance, Frustrating UX, and Eye-Watering Bills **Rating:** 5.0/5.0 stars **Reviewed by:** Jagdish S. | Associate Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** I run a data science team at a mid-sized company where we handle everything from messy data pipelines to heavy-duty machine learning. Databricks is the core engine of our stack. We use it to ingest raw customer telemetry, clean it up, and run massive PySpark jobs to train our predictive models. We also rely heavily on its MLflow integration to manage our model registry and handle deployments. Essentially, it's the infrastructure playground where all our heavy data lifting happens.The sheer raw performance is unmatched. If you are dealing with massive, bloated datasets that choke local machines or standard cloud instances, Databricks handles them like a beast. The managed Spark environment takes away a massive chunk of the infrastructure headaches involved in setting up clusters from scratch. From a pure data science perspective, having collaborative notebooks where my team can jump in, write Python or SQL concurrently, and instantly visualize data without switching tools is a massive plus. The MLflow integration is also fantastic; being able to track hyperparameters, log artifacts, and register models in the exact same workspace where the data actually lives saves us from fragmented tool sprawl and keeps our MLOps pipelines incredibly tight. **What do you dislike about Databricks?** The user experience can be deeply frustrating, and the platform often feels like a collection of entirely different tools taped together. The UI is clunky, unintuitive, and constantly changing, which means you waste time just trying to navigate the workspace. Debugging a failed Spark job is also an absolute nightmare—you have to dig through endless layers of convoluted driver and executor logs just to find a simple syntax or out-of-memory error. But my absolute biggest issue is the pricing structure. The billing is completely opaque. They charge you Databricks Units (DBUs) on top of your standard cloud provider's compute costs, and if a junior dev accidentally leaves a high-concurrency cluster running over the weekend without auto-termination strictly configured, you will face an eye-watering bill on Monday. **What problems is Databricks solving and how is that benefiting you?** Before moving to Databricks, our data engineering and data science teams were completely siloed. Engineers would dump files into cloud storage, and we would struggle to pull that data, map schemas, and train models without running out of memory. Databricks fundamentally solved this fragmentation. For instance, we recently built a real-time recommendation engine where we needed to process millions of daily user events. With Databricks, we built an end-to-end pipeline that handles the data engineering, trains the model, and exposes the model registry to our production environment under one roof. It cut our time-to-production from months to days, which, despite the UX headaches and high costs, makes it a necessary evil for a company handling data at our scale. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has been instrumental in improving your data science team's workflow and performance. We understand your frustration with the user experience and pricing structure, and we are constantly working to improve these aspects of our platform. Your feedback is valuable to us and will be shared with our team for further consideration. ### 27. Unified AI and Data Engineering Platform with Smooth Cost Control **Rating:** 5.0/5.0 stars **Reviewed by:** Jatin P. | Production Manager, Pharmaceuticals, Enterprise (> 1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** I appreciate how Databricks brings together data engineering, analytics, and machine learning processes in a single, governed workspace. The data reliability features like automatic versioning, transaction support, and quality controls are great for maintaining consistency and audit readiness without extra manual effort. For AI-related work, I find the experiment tracking, model deployment, and governance capabilities helpful for scaling efforts securely while meeting compliance standards. I also like the cost monitoring and cluster data management tools, which provide better visibility and help control expenses as usage grows across departments. The detailed breakdowns by job, cluster, user, and workload type, along with budget and alerts, are particularly useful. **What do you dislike about Databricks?** There is a learning curve when first adopting Databricks, especially for teams transitioning from traditional setups. The initial setup was a little difficult for these teams. **What problems is Databricks solving and how is that benefiting you?** Databricks unifies data engineering, analytics, and machine learning in one workspace, boosting data reliability. It helps scale AI efforts securely, while cost monitoring tools provide visibility and control as usage expands. **Official Response from Jess Darnell:** > We're glad to hear that you appreciate the unified workspace and data reliability features of Databricks, as well as the AI-related capabilities and cost monitoring tools. We understand that the learning curve and initial setup may be challenging for some teams, and we're continuously working to improve the onboarding process to make it easier for new users. ### 28. Unified Scalable Data Processing and Machine Learning Platform **Rating:** 4.0/5.0 stars **Reviewed by:** Anita P. | Business Intelligence Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** As a Data Scientist working for a mid-size company, my main use case for Databricks is as the central engine for all of our data processing and predictive modeling pipeline. I use it every day to pull raw dirty data from our cloud storage, explore it with complicated SQL queries and then create and train machine learning models with PySpark and Python. Basically it gives our data engineering and data science teams a common place to play on the same huge data sets at the same time without having to endlessly exchange files or credentials.From a day-to-day workflow perspective, I love the fluidity of the collaborative notebook environment. The ability to work with different languages in the same workplace is a great advantage. I can perform an optimized SQL query to pull in a hefty data set in one cell, then process it in the next using PySpark, and visualize it with Python libraries straight after. This fully removes the need to constantly bounce between different tools or IDEs. Another big victory for my daily work is the out-of-the-box connection with MLflow. It makes it very easy to roll back to a previous version, automatically tracks hyperparameter tuning, compares several model runs, and manages the full lifespan of a model. I really enjoy how Databricks takes away the effort of managing Spark clusters, you can spin up a distributed cluster with a few clicks, and focus on writing algorithms vs playing DevOps. **What do you dislike about Databricks?** And despite all its potential, working with Databricks does come with certain daily difficulties. What is most important for a mid-sized company like us is the aggressive pricing model for compute costs. The monthly payment can get out of control very rapidly, if you’re not compulsively watching your cluster configurations and auto-termination settings especially if a high-memory cluster is unintentionally left operating over the weekend. Another major pain point is the built-in Git integration. Databricks Repos has been helpful however managing complicated merge conflicts or branch management still feels unexpectedly clumsy compared to a regular local IDE like VS Code. Lastly, the learning curve is rather severe for new employees. The user interface might be complicated and debugging distributed computing failures can be a major bottleneck for young data scientists getting up to speed. **What problems is Databricks solving and how is that benefiting you?** The largest basic problem that Databricks tackled for our business was breaking down the separate silos between our data engineers and data science team. We saw this effect in the real world recently when we were working on a project to build a fraud detection algorithm. In the prior approach, I would have to submit a ticket to data engineering, wait days for them to extract and clean the data, and then try to train the model locally. I would get out of date data by the time I got it, and my machine would crash all the time owing to memory constraints. I could immediately connect to our Delta Lake, utilize PySpark to process the huge data size without any memory issues and train the model on a scalable cluster, all in the same ecosystem using Databricks. This one-stop-shop decreased our model deployment duration from about a month to a couple of days, dramatically enhancing how fast we offer actionable business value. **Official Response from Aunalisa Arellano:** > We're glad to hear that Databricks has been able to streamline your data processing and predictive modeling pipeline, and that you find the collaborative notebook environment and multi-language support advantageous for your day-to-day workflow. ### 29. Excellent for big data team but very tricky to manage costs and access **Rating:** 4.0/5.0 stars **Reviewed by:** Keshav R. | Senior System Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** The main benefit from IT side is that Databricks removes the infrastructure headache. Earlier our data engineers were always asking for setting up Spark clusters, managing libraries, and handling VM failures. Databricks does all this automatically. The auto-scaling is quite smooth; it adds nodes when workload is high and removes them later, so infrastructure utilization is very efficient. Also, the integration with AWS and Azure IAM roles is very solid. We can easily connect it with our active directory for single sign-on SSO, which makes user onboarding very fast. The notebook sharing feature is also liked by my teams because they can collaborate without sharing code files over email or Slack. **What do you dislike about Databricks?** The biggest pain point for IT Operations is cost control. Databricks billing uses DBUs Databricks Units, and it is very difficult to predict monthly budget. Another issue is the cluster startup time. It takes around 4 to 7 minutes to spin up a new cluster. **What problems is Databricks solving and how is that benefiting you?** We are using Databricks to centralize our entire data processing and machine learning pipelines. Before this, data was scattered in different silos, and maintaining different environments for data engineers and data scientists was an operational nightmare. Now, Databricks gives a single platform. From an operations perspective, it reduces my team's support ticket load by at least 40% because users can self-serve their clusters within the limits we set. It saves a lot of engineering hours that we used to spend on maintaining open-source Apache Spark infrastructure. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks has helped centralize your data processing and machine learning pipelines, reducing operational complexity and support ticket load for your team. We recognize the importance of addressing cost and cluster startup time concerns, and we appreciate your insights as we strive to enhance the Databricks experience. ### 30. Managed Spark Clusters and Collaborative Notebooks That Just Work **Rating:** 4.5/5.0 stars **Reviewed by:** Ranjit P. | Cloud Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** The best thing about Databricks is the managed Spark clusters. Earlier, setting up Apache Spark manually on AWS or Azure was a big headache. Now, with Databricks, I can spin up a cluster with just a few clicks. The auto-scaling feature works very well, when processing heavy data workloads, it automatically adds nodes and reduces them when done, which saves some cloud costs. Also, the collaborative notebooks are amazing. My team members and I can work on the same Python or SQL code at the same time, just like Google Docs. The integration with Delta Lake is also a big plus because it gives ACID transactions directly on cloud storage, so data corruption issues are very rare now. **What do you dislike about Databricks?** The biggest issue is the pricing. Databricks DBUs Databricks Units are quite expensive, and if you are not careful with cluster configurations or leave a cluster running by mistake, the cloud bill will jump very high quickly. The cost management tools inside the platform could be much better. **What problems is Databricks solving and how is that benefiting you?** We are solving the big problem of data silo and slow ETL Extract, Transform, Load pipelines. Before Databricks, our data science team and data engineering team were working in different environments, and moving data between them was painful. Now, Databricks acts as a single Unified Analytics Platform. We ingest raw data into Azure/AWS, clean it using Spark SQL, and the machine learning guys use the same platform to train models. It has reduced our data processing time from hours to minutes, which helps us deliver client projects much faster. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks has helped to solve the challenges of data silos and slow ETL pipelines for your team. We are committed to providing a unified analytics platform that enables seamless collaboration and faster data processing for our users. ### 31. Comprehensive Ecosystem, Complex Setup **Rating:** 5.0/5.0 stars **Reviewed by:** Eleazar C. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like the most about Databricks is the whole ecosystem. It's not easy to have everything you need in a single platform that already has access to the data by its nature. You don't have to handle complex integrations for new projects like data engineering, machine learning, creating dashboards, or developing applications. **What do you dislike about Databricks?** I think Databricks can improve in the complexity. It gets difficult or tricky because there are plenty of things and features, and at some point, it becomes complicated to catch all of them. The user experience can improve, especially for stakeholders that are not 100% technical. It's not easy to set up; you need to set up a lot of things, and when you just start, it's really complicated to get things done. **What problems is Databricks solving and how is that benefiting you?** Databricks integrates different system sources, manages high data volumes with distributed computation, and keeps storage costs low. **Official Response from Jess Darnell:** > We appreciate your feedback on the complexity of Databricks. We are constantly working to improve the platform and make it more user-friendly, especially for those who are not fully technical. Thank you for bringing this to our attention. ### 32. Prominent when scaling LLMs and pipelines, but be mindful of the cloud bill! **Rating:** 4.5/5.0 stars **Reviewed by:** Anupama J. | Junior Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** As a researcher of AI, it seems like infrastructure is the number one problem, especially setting up clusters, building drivers, and scaling distributed training. Databricks takes care of all that by itself. I can easily and quickly deploy a cluster of nodes for GPUs with PyTorch and DeepSpeed preconfigured in a few clicks. This built-in MLflow is a lifesaver to keep track of experiments. All the hyperparameters or architecture changes with respect to an embedding model are automatically being tracked every time. ESSENTIAL: I no longer have to struggle to get clean and versioned datasets from data engineers for training purposes when working with Delta Lake. Getting around those feature stores is also very easy with the Unity Catalog. **What do you dislike about Databricks?** First, it's really expensive, brother. On an extremely large A100 GPU cluster, if you, or someone on your team, forget to configure the auto-terminate, you are going to have a very bleak day with finance tomorrow. Expenses can add up quickly. Additionally, although they are too lightweight to be an ideal platform for distributed deep learning, the debugging workflow may be tedious. The intersection of the computing nodes makes it difficult to find the exact PyTorch-Out-Of-Memory or CUDA-Out-Of-Memory error occurring in the Spark logs. I also feel like the native MLflow UI in Databricks isn't as advanced and specialized as some of the tools like Weights & Biases. **What problems is Databricks solving and how is that benefiting you?** It fills the oceanic yawning void between research in AI and data engineering. To get terabytes of unstructured text data pre-trained in the past was a multi-step nightmare in different environments. I can make heavy data preparation using Spark and immediately switch to Python for training my model in the same ecosystem. It helps to communicate goodwill amongst the entire team. Everything is in one workspace, so my transition of raw data to experiment tracking to finally registering the model in the registry is done in one, unified, pipeline. **Official Response from Jess Darnell:** > Thank you for your feedback - we appreciate your review! ### 33. Databricks is super fast with big data, yet slow to learn. **Rating:** 4.0/5.0 stars **Reviewed by:** Khushi S. | Data Analyst Intern, Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** I work as a Data Analyst and every day, I use Databricks to complete my data tasks. The best thing I like is the processing speed. We were loading large tables and it was taking too long before we could load big tables using normal databases. My rich SQL queries are very fast in Databricks due to the use of Apache Spark backend. In addition, the Notebook feature is quite useful to me. I can create SQL code in a cell and in the next cell, I can write Python or Pandas code to do some particular data cleaning. It is also easy to connect Databricks to our Power BI dashboards. **What do you dislike about Databricks?** There are some things which I am facing issues with. First is the cluster starting time. In the morning, it takes 5-10 minutes to boot but once I log in. When the management requests urgent report, then I must make myself sit and wait till the cluster turns green. **What problems is Databricks solving and how is that benefiting you?** Its primary issue that it is resolving is the ability to process large volumes of company data without system freezing. Previously, it was a pain to deal with millions of rows. I can now easily query, filter and aggregate large datasets. It is helping my team since data engineers and data analysts are sharing the same workspace. In case data engineers make a new table, I can see it right away and can query it directly in my notebook. Using notebooks to share with other team members to have it reviewed is similar to using Google Doc, which makes my everyday reporting work incredibly quick. **Official Response from Jess Darnell:** > We appreciate your feedback on the benefits of using Databricks for processing large datasets and the seamless collaboration between data engineers and data analysts. We acknowledge the issue with cluster starting time and will strive to enhance the performance in this area. ### 34. Best tool to work with big dealer data, but requires technical team. **Rating:** 4.5/5.0 stars **Reviewed by:** Dilkash N. | Assistant Sales Manager (Institutional Sales), Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** We have a very big dealer and distributor network throughout India in our sanitaryware business. Big sales data are being generated everyday. Speed is my favorite thing about Databricks. Whenever we use simple excel or old software before, it is always hanging. And now our company data team is churning through the millions of rows in a short time. As a Senior Sales Specialist, I am making sure that I get my territory dashboard and forecasting reports at least daily in the morning. It is bringing all disperse data together in a good manner. **What do you dislike about Databricks?** Worst thing is that it is highly technical software. As a sales person, I cannot apply it in locating data directly. I need to request data engineering or IT team to code or make query every time I desire some new custom report. User-non technical interface is becoming very complicated. And my management is continually saying this costs a great deal. **What problems is Databricks solving and how is that benefiting you?** We are solving target tracking and inventory matching problem. We have a wide range of products, such as tiles, faucets, and washbasins, in Cera. Databricks is assisting our company to understand what region is selling what product more and the trend in the market. My advantage is due to this, because I can advise my local dealers accordingly, as to next month order. It is providing highly precise sales forecast and saving me my manual reporting time and I am closing my sales targets with ease. **Official Response from Jess Darnell:** > We're glad to hear that Databricks is helping you with speed and data consolidation. We understand the challenges of technical complexity and will continue to work on improving the user interface for non-technical users. ### 35. Databricks Unifies Data Engineering, Analytics, and ML for Faster Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Rudi T. | Cloud Platform Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** What I like most is how Databricks brings data engineering, analytics, and machine learning together in one environment. Our teams no longer need to jump between multiple tools to build pipelines, analyze data, and train models, which keeps work more consistent and streamlined. The notebook experience is genuinely collaborative and helps us move from exploration to development much faster. Integration with Spark and Delta Lake also makes it easier for us to process large datasets efficiently and stay organized as projects grow. **What do you dislike about Databricks?** The platform can feel overwhelming for new users due to the sheer number of features available. Some of the more advanced configurations also require a solid understanding of cloud infrastructure and cluster management, which can add to the learning curve. Cost monitoring needs close attention as well, especially for teams that run large workloads on a frequent basis. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us modernize our data platform and replace several disconnected tools. We now use it as a single place for ETL processing, analytics, and machine learning workloads. As a result, our operational complexity has gone down, and collaboration between data engineers and analysts has improved. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' unified environment and collaborative notebook experience helpful for your teams. We understand that the platform may feel overwhelming for new users, and we're continuously working on improving the user experience and providing more resources for learning and support. ### 36. Streamlined Data Integration with Robust Collaboration **Rating:** 5.0/5.0 stars **Reviewed by:** Samuel D. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** I like Databricks for its centralized UI that allows for seamless development, collaboration, and deployments. I appreciate the Unity Catalog Governance, which helps with data sharing in a controlled yet federated manner. The platform's capability for seamless data import, quick retrofit pipelines, and automation makes my tasks more efficient. Additionally, bringing disparate data sources together and automating with notebooks to rewrite ETL processes was easy, and it quickly sped up the onboarding of legacy ML models. **What do you dislike about Databricks?** DABs are limited to notebooks. I want it for DLTs, ML models, Genie. GitHub integration is confusing with deployment handoffs and collaboration. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for reporting and ML models, solving real-time analytics and time to value. The centralized UI aids in development and collaboration, while Unity Catalog helps with data sharing. It's easy to bring disparate data sources and automate with notebooks for ETL. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks' centralized UI and Unity Catalog Governance helpful for seamless development and data sharing. We appreciate your feedback on the limitations with DABs and GitHub integration, and we'll take that into consideration for future improvements. ### 37. A Game Changer for Unifying Data Engineering and ML, but Watch Your Compute Costs **Rating:** 5.0/5.0 stars **Reviewed by:** Lokesh S. | Senior Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** May 28, 2026 **What do you like best about Databricks?** As a Senior Data Scientist at a mid-sized tech company, I've used Databricks for the last couple of years, and it has really transformed our data teams. The main use case we want to process large amounts of user interaction data to create predictive models, namely customer churn, recommendation engines, and customer lifecycle value (LCV) estimation. Prior to Databricks, our workflows were very disjointed. The data engineers utilized one set of complicated tools for ETL tasks and the data scientific research group utilized completely various neighborhood environments for modelling. Databricks gave everyone a common platform and workspace, a cloud-based experience, which put everyone together under one roof.I like the ability to work with collaborative notebooks together with great computing at the same time the most. It's a significant productivity win to be able to code in Python, SQL and Scala — and, vitally, do so in the same environment as the data engineers who are creating the core pipelines. Additionally, I find the out-of-the-box integration with MLflow to be a game-changer in my workday routine. It eliminates the pain of managing version registries, tuning parameters, and more complicated model experiments. I have to also point out the ease with which they have improved cluster management. As a data scientist, I need to use a heavy machine learning model, one that is not always in use for the duration. I can start a distributed, powerful Spark cluster in a few clicks, train my model on it, and then quickly configure it to automatically kill itself after the job completes - I do not want to waste resources. **What do you dislike about Databricks?** The platform doesn't have exceptional user-friendliness, though, and there are some drawbacks that you'll need to navigate with care. The greatest disadvantage is the loss of control of spending if you're not careful. With so much of the complicated back-end infrastructure abstracted away, it's quite easy for a newer team member to provision an unnecessarily large compute cluster or forget to switch on auto-termination, with a very unpleasant surprise on the monthly billing statement. One must be careful about creating rigid rules for use of the workplace and tracking how it is used. Moreover, it can be unpredictable to learn the learning curve of a distributed computing paradigm that is different from the one analysts or data scientists already have experience with, such as Apache Spark. They have come a long way in introducing features that are similar to the standard Python library but for complex distributed errors, a lot of knowledge about the inner workings is still needed for debugging. The user interface can also sometimes be a bit slow and cumbersome when working with deep levels of workspace folders in which there are hundreds of notebooks from the legacy version. **What problems is Databricks solving and how is that benefiting you?** As for real-world problems solved, Databricks did away with that well-known situation of having a predictive model I love on my laptop, but not at all in the production environment. We standardized our runtime environments throughout the entire organization and set up MLflow for deployment, saving us many painful weeks to only a couple of days for our models to go to production. Yet another huge success for our company is that we removed the silos among our departments. We do not now throw a clean data table over a metaphorical wall for me to analyse second thoughts because I am not a data engineer. When I recently had to work with a complex and custom feature that was created for an algorithm that recommends products for users, I wrote this feature with the data engineering lead in a common notebook on Databricks, and we tested and optimized this pipeline together. Historically, we would have deployed it the wrong way the first time, but that wouldn't have worked with our previous infrastructure! It has really made our team a very effective cross functional team! **Official Response from Jess Darnell:** > Thank you for sharing your experience with Databricks! We're glad to hear that it has transformed your data teams and provided a common platform for collaboration. We appreciate your feedback on the user-friendliness and cost control, and we are continuously working to improve in these areas. It's great to hear that Databricks has helped standardize runtime environments and removed silos among departments, leading to more efficient cross-functional teamwork. ### 38. Powerful Platform with Easy Data Migration C **Rating:** 4.5/5.0 stars **Reviewed by:** Nitin A. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks provides a unified platform for data engineering, analytics, and AI, reducing migration complexity. * It supports seamless integration with cloud storage, databases, and legacy data platforms. * Built-in scalability allows organizations to migrate from on-premises or traditional data warehouses without major infrastructure changes. * With automated optimization and open formats like Delta Lake, data migration becomes faster, more reliable, and future-proof. **What do you dislike about Databricks?** The interface could be better and The platform can have a steep learning curve for new users, especially when managing clusters, jobs, and workspace administration. * Cost management could be more transparent, as compute and storage expenses can grow quickly without proper monitoring. * Debugging and troubleshooting distributed workloads can sometimes be challenging compared to traditional environments. * Some enterprise features and integrations require additional configuration, which can increase setup and operational complexity. **What problems is Databricks solving and how is that benefiting you?** Replacement of synapse pain points **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a powerful platform for data engineering, analytics, and AI, with seamless integration and built-in scalability for data migration. We understand your concerns about the interface, learning curve, and cost management. We are constantly working to improve the user experience and provide more transparent cost monitoring. ### 39. Databricks Boosts Productivity with a Unified Workspace and AI-Assisted Development **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** May 27, 2026 **What do you like best about Databricks?** As an ADE, what I like most about Databricks is that it removes infrastructure friction, so I can focus purely on data engineering logic. I also really appreciate the unified workspace: I can write PySpark for data extraction and transformation, switch to SQL for exploratory analysis, and review data lineage, all within a single browser tab is a huge productivity boost. On top of that, the built-in AI features have been incredibly helpful because they let me worry less about syntax and spend more time on the logic itself. Finally, with the seamless integrations through Lakehouse Federation and the straightforward onboarding, my work has become much smoother. **What do you dislike about Databricks?** While the platform is excellent for development, the DBU consumption model and cluster management can feel a bit daunting at my level. As a beginner, I spent a lot of time testing different bits of logic, and it was easy to forget to terminate the all-purpose cluster afterward, which led to minimal but still unnecessary credit consumption. Thankfully, auto-termination exists and helped keep credits from disappearing. Still, a more aggressive auto-termination setting or a smarter pause feature would make it easier to avoid any credit loss in the first place. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the local environment hell-hole that often slows down junior engineers. By providing a ready-to-use Lakebase architecture, it lets me practice enterprise-level data engineering without needing to connect to VPNs or deal with complex Docker setups. In my project, it addressed the full flow: ingesting raw data, transforming it, and serving it for analytical queries. This also benefits my team, because I can onboard onto real data pipelines much faster and start contributing sooner. At the same time, I’m learning how to build production-ready ETL workflows without my senior teammates having to spend hours helping me troubleshoot my local Python/Spark environment. An unexpected benefit was how seamless collaboration is. Because the notebooks are cloud-based and ties to the workspace, sharing my project with senior engineers for code reviews was as simple as sending a link. Additionally, the way Databricks handles metadata made me realize early in my career how important data governance is. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been such a productivity boost for you! The unified workspace and AI-assisted development are indeed powerful features that many of our users appreciate. We understand your concerns about the DBU consumption model and cluster management. We're constantly working to improve the user experience, and your feedback will be taken into consideration for future enhancements. ### 40. Centralized Governance, Powerful Migration Tool **Rating:** 5.0/5.0 stars **Reviewed by:** Jaswanth J. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 26, 2026 **What do you like best about Databricks?** I like the Unity Catalog as a single governance layer which centralizes access control and offers fine-grained permissions across data assets. The workspace API and automation features are valuable for streamlining operations. I appreciate that volumes replace mounts, improving security with credential-free access. The Lakehouse Federation simplifies cost consolidation and reduces data movement costs. Having Photon and ML Runtime on the same platform enhances operational efficiency. The initial setup was user-friendly, thanks to the guidance from the Databricks portal. **What do you dislike about Databricks?** * Migration tooling is manual and fragmented * Mount-to-Volume path conversion has no automated path * Cluster security mode NONE still exists * Hive metastore and UC coexist awkwardly * Custom WHL libraries on mount lack a clean upgrade path **What problems is Databricks solving and how is that benefiting you?** Databricks provides centralized access control with fine-grained permissions, identity-based access without exposing storage credentials, unified data discovery and lineage, and reduces operational overhead by consolidating platforms and managing data more efficiently. **Official Response from Jess Darnell:** > We're glad to hear that you appreciate the Unity Catalog and the workspace API for streamlining operations. We understand your concerns about the manual and fragmented migration tooling, and we are continuously working to improve this aspect of our platform. ### 41. Efficient Data Management, Needs More Granular Permissions **Rating:** 3.0/5.0 stars **Reviewed by:** Matt G. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks to manage my enterprise data, and it solves the issue of sharing data at scale for reporting and analytics with my customers. I value Delta sharing, now called open sharing, because it allows me to share data at scale without requiring customers to set up specific APIs or virtual pipelines. I also think that the initial setup of Databricks was fairly easy. **What do you dislike about Databricks?** I don't like how Databricks lacks the ability to share data with more granular permissions. The ability for delta sharing to take predicate pushdowns and enable that in data sharing to customers would be helpful. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to manage my enterprise data and solve the issue of sharing data at scale for analytics with my customers. I value the Delta sharing feature, as it allows data sharing without needing specific APIs or pipelines. **Official Response from Janelle Glover:** > Thank you for sharing your experience with Databricks. We're pleased to hear that the initial setup was easy and that Delta sharing has been valuable for sharing data at scale. We understand your feedback about the need for more granular permissions and will take it into consideration for future improvements. ### 42. Fast, Efficient Databricks with Strong Ecosystem Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Johnson C. | Software Engineering Intern - Data Science, Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I love how databricks closely integrated with the broader data ecosystem, and it runs fast and efficiently. It makes it easier to integrate new features and bring new additions into the ecosystem whenever something comes up, or whenever issues need to be addressed. **What do you dislike about Databricks?** March I think it has a lot of integration a lot of tools. I just hope that one thing they could do that they could build some sort of like an agent skillet. They already have a skill set, but like Aiden skills so they can teach me that potentially have already exists and I know they have really good MCPs and skills for the agent to use already. **What problems is Databricks solving and how is that benefiting you?** It is solving how I trained the model how I run the motor pipelines and how I store and save the track of model training process **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the strong ecosystem integration and the fast and efficient performance of Databricks. We appreciate your feedback and will take your suggestion for an agent skill set into consideration for future improvements. ### 43. An All-in-One Platform for Data, Analytics, and Machine Learning **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Entertainment | Enterprise (> 1000 emp.) **Reviewed Date:** May 29, 2026 **What do you like best about Databricks?** I really value that this platform supports everything from raw data ingestion and SQL analytics to machine learning with notebooks. It’s not just another external tool; it feels like a fully integrated solution for an entire organization. I also appreciate that it’s designed to support both technical and business users. **What do you dislike about Databricks?** Managing costs and optimizing cluster usage can sometimes be challenging and requires internal knowledge of the underlying architecture, such as CPU and RAM configuration for jobs. This can significantly impact the overall budget, especially for small companies. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us process millions of records daily in a reasonable amount of time while maintaining scalability for our solutions. It also allows us to build and integrate solutions not only within Databricks itself, but also by deploying external packages. In addition, the command-line tools provide flexibility to integrate with our current CI/CD workflow, helping us reduce deployment times. **Official Response from Jess Darnell:** > We appreciate your feedback on the benefits of Databricks for processing large volumes of data and integrating with external packages. We understand the impact of cost management on small companies and are focused on providing solutions to address this concern. ### 44. Centralized Dashboard with Smooth, Cost-Saving Autoscaling **Rating:** 4.5/5.0 stars **Reviewed by:** Kimberly G. | Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 29, 2026 **What do you like best about Databricks?** Everything is centralized is a single dashboard spark jobs, notebooks and data pipelines. Autoscaling and auto termination genuinely help keep costs under control, and we could was a pleasant surprise that both run smoothly without any noticable lag. Sharing notebooks with the team is straightforward and cuts down on alot of back and forth. **What do you dislike about Databricks?** Finding older queries is really paunful. Anything beyond a few weeks becomes hard to track down, which makes it difficult to keep my data to day work flowing smoothly and to continue working without constant interruptions. **What problems is Databricks solving and how is that benefiting you?** We run ETL and ML workloads without having to worry too much about the underlying infrastructure. I can also manage inventory information, at least to some extent, without opening a bunch of different tabs. I spend less time troubleshooting clusters and more time actually working with the data. **Official Response from Janelle Glover:** > It's fantastic to hear that Databricks is helping you run ETL and ML workloads seamlessly, allowing you to focus more on working with the data and less on managing infrastructure. We're thrilled to be a part of your success. ### 45. Unified Data Platform with Fantastic Usability **Rating:** 5.0/5.0 stars **Reviewed by:** Jared C. | Undergraduate Research Assistant, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like how tightly integrated everything is in Databricks. My company's data and the machine learning experiments I need to do are right there, along with the AI BI dashboards, which are easily accessible and shareable. It was super easy to set up, as my boss just added me to a credential list and I got access immediately. Databricks unifies compute and storage in a way I've never had before, making it really easy to run queries efficiently, quickly, accurately, and securely. **What do you dislike about Databricks?** Configuring the compute can be a little bit challenging, and sometimes, it's difficult to access the resources that I need. But when everything comes together, it works nicely. **What problems is Databricks solving and how is that benefiting you?** Databricks unifies compute and storage, making it easy to run queries efficiently, quickly, accurately, and securely. It meets all my company’s data science and engineering needs. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We appreciate your feedback about the challenges with configuring the compute, and we are committed to enhancing the platform to address these issues. ### 46. Consolidated Our Data Stack with Databricks that Boosted Performance and Productivity **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Retail | Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2026 **What do you like best about Databricks?** Coming from an Airflow + Snowflake setup, moving to Databricks removed a layer of coordination overhead we had normalized, jobs scheduling jobs, reverse ETL pipelines just to get analytical results back into operational systems, and a separate feature store drifting out of sync with training data. The integrations were a big part of why the transition was smoother than expected: native connectors for cloud storage, Git-based repo sync for version-controlled notebooks, and the Databricks SDK plugging cleanly into our existing CI/CD pipelines meant we weren't rebuilding everything from scratch. Databricks Workflows replaced our Airflow DAGs cleanly, Unity Catalog gave us lineage and access control across our full medallion architecture without a separate tool, and Lakebase let us retire the online feature store entirely since model features now live where the data already is. Performance on large-scale aggregations across our brick-and-mortar store datasets improved noticeably, and the workspace UI makes it easy for the whole team to navigate notebooks, pipelines, and catalog without context-switching. The AI-assisted features in the notebook environment genuinely speed up development. The autocomplete and error suggestions that understand the data context are more useful than they sound day-to-day. Onboarding new engineers was also faster than expected given the depth of the platform, with thorough documentation and a responsive support team during migration. From an ROI standpoint, consolidating tooling meant fewer vendor contracts, less pipeline maintenance, and engineering time redirected toward actual product work. **What do you dislike about Databricks?** The cost model is the most persistent friction point — compute costs can escalate quickly if cluster lifecycle management isn't tight, and for a team that's still maturing its governance around who spins up what, the billing visibility could be more granular out of the box. The UI, while generally clean, gets harder to navigate at scale; when you have dozens of workflows, notebooks, and catalogs, the workspace organization tools don't quite keep up with the sprawl. On the integrations side, some third-party connectors feel like they were added as an afterthought — the experience isn't always as seamless as the native ones, and occasional version compatibility issues have caused unexpected debugging time. Performance on very large unoptimized queries can still surprise you with cold start latency on serverless compute, which matters when you're iterating quickly during development. The AI assistant features are improving but still inconsistent — context awareness drops off on complex multi-file projects and the suggestions occasionally miss the mark in ways that slow you down rather than help. Support response quality has been good for critical issues, but for nuanced technical questions the first response is sometimes generic, and getting to someone with deep product knowledge takes an extra round of escalation. **What problems is Databricks solving and how is that benefiting you?** The core problem we were solving was operational sprawl — we had analytical data living in one place and operational data in another, with a fleet of pipelines just to keep them in sync. Working with high-volume brick-and-mortar store data across a medallion architecture, the performance gains on large aggregations alone justified the move; queries that previously required careful warehouse sizing now handle gracefully on autoscaling compute. Consolidating onto one platform also meant our AI and ML workflows stopped being second-class citizens — feature engineering, model training, and serving now happen in the same environment where the data lives, which removed an entire category of infrastructure we were maintaining. The workspace UI, while not perfect at scale, made it easier to onboard the broader team without everyone needing deep platform expertise to be productive from day one. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has had such a positive impact on your data stack, boosting performance and productivity. It's great to know that the integrations, performance improvements, and AI-assisted features have made such a difference for your team. We appreciate your feedback and are committed to continuously improving our platform. ### 47. High-Performance Analytics with Databricks SQL and Unity Catalog Governance **Rating:** 4.0/5.0 stars **Reviewed by:** Arpit V. | Data Platform Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** Databricks delivers excellent performance when working with large volumes of data. The Databricks SQL environment also makes it easier for business users and analysts to explore insights without having to rely so heavily on engineering teams. Features such as Unity Catalog strengthen governance and simplify managing access across departments. **What do you dislike about Databricks?** The learning curve can feel steep, especially for users coming from traditional data warehouse solutions. In my experience, query optimization can also take some extra effort, since certain workloads require additional tuning to get the best results. **What problems is Databricks solving and how is that benefiting you?** We needed a solution that could handle both data warehousing and advanced analytics without creating new data silos. Databricks has helped us centralize our data assets while still maintaining strong governance. As a result, teams can access trusted data more quickly and build reports with fewer delays, which has improved decision-making across the organization. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been delivering excellent performance for your large volumes of data and that the SQL environment is making it easier for business users and analysts to explore insights. ### 48. Seamless Data Integration, Amazing Customer Service **Rating:** 5.0/5.0 stars **Reviewed by:** Alwarda F. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I appreciate the seamless integration that Databricks offers, making it very easy to use and allowing it to integrate with different systems. It's great for automating code and building pipelines, and the customer service is amazing. I think the apps are amazing too. Databricks is one of the most self-intuitive products, and the integration process is straightforward. The learning curve is steep initially, but it becomes easier as you progress. **What do you dislike about Databricks?** I would love to see that database has been generated within Databricks so I don't have to move my data outside of Databricks. I can build the graphs and connect it to Genie's. I think that's missing. From data. **What problems is Databricks solving and how is that benefiting you?** Databricks integrates data seamlessly from different sources, is easy to use, and integrates well with other systems. It automates code development, enhancing efficiency. **Official Response from Jess Darnell:** > We're happy to hear that you are benefiting from Databricks' seamless data integration and automation capabilities. We understand your suggestion about generating databases within Databricks and will take it into consideration for future improvements. Thank you for your valuable input! ### 49. Streamlined AI and Data Solutions with Swift Setup **Rating:** 4.5/5.0 stars **Reviewed by:** Amrendra S. | Solution Delivery Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks as a mainstream tool to implement native AI and data solutions worldwide and for Accenture. It's heavily used in engagements to enable data analytics for our clients. The Accelo Unity Catalog is valuable as it helps accumulate metadata and facilitates analytics. The deployment scripts provided make it easy to deploy in our environment quickly, which is great since it allows us to start working immediately. I also find the Genesys catalog's support for AI agents fantastic, greatly aiding adoption. **What do you dislike about Databricks?** The aspect of the unit bridge used to be an issue for us when integrating different stacks and AI agents. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to implement AI and data solutions, enabling data analytics for clients. It simplifies deployment with scripts making it easy to start working in different environments. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in enabling data analytics for your clients. We appreciate your feedback on the deployment process and the support for AI agents. ### 50. All-in-One Platform for Data Engineering, ML, AI, and Data Management **Rating:** 4.5/5.0 stars **Reviewed by:** Akanksh M. | Machine Learning Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** It brings all the tech stacks together in one platform—data engineering, machine learning, AI, and data management—so everything is in one place. It also includes advanced features that make the platform feel complete and capable. **What do you dislike about Databricks?** We need more open-source, direct connectors to both legacy and current-generation platforms to enable better data extraction. These connectors should support real-time extraction as well as real-time data rendering. **What problems is Databricks solving and how is that benefiting you?** It brings all types of data into one place, which makes data and access management easier. I can build data warehouses and then downstream the data to AI BI dashboards and ML models, which is very useful. Special features like the feature store, serving endpoints, AI BI dashboard, and Genie help me understand the data, work with it more effectively, and ultimately reach my goals. **Official Response from Janelle Glover:** > Thank you for sharing what you like best about Databricks. We're glad that you're enjoying the feature store, AI BI dashboard, and Genie. We understand the importance of open-source connectors and real-time extraction, and we are continuously working to enhance our platform to better meet your needs. ## Databricks Discussions - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [View Databricks pricing details and edition comparison](https://www.g2.com/products/databricks/reviews?page=2§ion=pricing&secure%5Bexpires_at%5D=2026-07-04+21%3A02%3A23+-0500&secure%5Bsession_id%5D=dc44f7b0-68e3-4a2d-89a4-ad270f87512a&secure%5Btoken%5D=cbef552d829f1f8e407769e3723d44dd2340a1f93b94d04a3fa1ba4275c2c8e1&format=llm_user) ## Databricks Integrations - 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[SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) - [Spark SQL](https://www.g2.com/products/spark-sql/reviews) - [Spotfire Analytics](https://www.g2.com/products/spotfire-analytics/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) - [Workday HCM](https://www.g2.com/products/workday-hcm/reviews) ## Databricks Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics **Management** - Data dictionary - Data Replication - Query Language - Data Modeling - Performance Analysis **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Customization - AI Agent Builders** - Natural Language Configuration - Tone Customization - Security Guardrails **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Traffic Management & Performance - AI Gateways** - Token-Aware Rate Limiting - Semantic Caching - Multi-Model Routing & Fallbacks **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Maintenance** - Data Migration - Backup and Recovery - Multi-User Environment **Security** - Access Control - Roles Management - Compliance Management **Operations** - Metrics - Infrastructure management - Collaboration **Analytics** - Analytics capabilities - Dasboard visualizations **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Functionality - AI Agent Builders** - Omni-channel Support - Agent Branding - Proactive Response Capabilities - Seamless Human Escalation **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Integrations** - Hadoop Integration - Spark Integration **Data Quality** - Data Preparation - Data Distribution - Data Unification **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Deployment** - On-Premise - Cloud **Security** - Data Encryption - User Access Control **Maintainence** - Data Quality Management - Policy Management **Management** - Cataloging - Monitoring - Governing **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Data and Analytics - AI Agent Builders** - Analytics & Reporting - Contextual Awareness - Data Privacy Compliance **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Performance ** - Scalability **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Integration - AI Agent Builders** - Workflow Automation - API Usage - Platform Interoperability - CRM Data Integration **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Generative AI** - AI Text Generation - AI Text Summarization **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Governance** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Decision Making **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Generative AI** - AI Text Generation - AI Text Summarization **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - 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