--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1363 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: 1363 scale: '5' date_modified: '2026-08-10' 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:** 1,363 ## About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, 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 praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications. (192 reviews) - Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services. (154 reviews) - Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency. (141 reviews) - Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights. (114 reviews) - Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization. (112 reviews) - Scalability (111 reviews) - ML Integration (106 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (102 reviews) - Machine Learning (97 reviews) - Users value the **effective data management features** of Databricks, simplifying their workflows and enhancing decision-making. (87 reviews) **What users dislike:** - Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability. (78 reviews) - Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects. (71 reviews) - Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools. (64 reviews) - Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes. (45 reviews) - Users face **complex setup** challenges initially, though support helps simplify the experience over time. (35 reviews) - Performance Issues (34 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (34 reviews) - Poor UI Design (33 reviews) - Users express frustration over **missing features** in Databricks, limiting its effectiveness for complex deployments and custom setups. (31 reviews) - Cost (29 reviews) ## Databricks Reviews ### 1. Helpful for Rider Service Operations Reporting **Rating:** 4.0/5.0 stars **Reviewed by:** Jayesh W. | Software Engineer II, Computer Software, Enterprise (> 1000 emp.) **Reviewed Date:** July 20, 2026 **What do you like best about Databricks?** What I like most about Databricks is that it helps our team monitor rider service operations more efficiently. We use Databricks Dashboards and SQL Warehouses to track daily revenue, service requests, customer satisfaction scores, completed services, and failed transactions in one place. It has made operational reporting much faster and allows us to identify trends without manually preparing multiple reports. **What do you dislike about Databricks?** One thing I found slightly challenging with Databricks was understanding how different components such as SQL Warehouses, dashboards, and permissions work together when building operational reports. Setting up dashboards for rider service metrics took some time initially. After getting familiar with the workflow, it became much easier to manage, but better onboarding guidance for first-time users would improve the overall experience. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us centralize rider service operations analytics in a single workspace. We use it to track daily revenue, service requests, customer satisfaction scores, completed services, and failed transactions through operational dashboards. Before using Databricks, preparing reports across multiple datasets was more time-consuming. Having these business metrics available in one place has significantly improved reporting efficiency and helped our team identify operational trends much faster. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been helpful in monitoring rider service operations efficiently. Our dashboards and SQL Warehouses are designed to centralize and streamline operational reporting, making it easier to track key metrics in one place. We appreciate your feedback around onboarding and we are continuously working to improve our onboarding process to provide better guidance for new users. ### 2. Databricks in my case: Multiple Integrations, Intuitive UI, and Reliable Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Yelnur K. | Schedule Manager, Airlines/Aviation, Mid-Market (51-1000 emp.) **Reviewed Date:** May 19, 2026 **What do you like best about Databricks?** What I like most about Databricks is its Integrations part. In workplace, we integrate Database within multiple data soucres. Also, I can't complete my review without mentioning UX and UI design, which makes the overall workflow feel intuitive and genuinely user-friendly. When it comes to speed of the processes, it never offended us. It works as expected. Comparitevly from the market pricing, the price of the service is quite reliable for us. There is a help center of the Databricks, if you can't find any answers to your questions, there are specialists that may assist you with your inqurires. As an instance, I can remember the case where we had an issue within exam process, they helped us to solve this problem. **What do you dislike about Databricks?** From dislikes the ai quality of Genie. Guys it could be improved, especially the reasoning part. Also, I can say the case when we had an issue with exam process. Specialists helped us, but it took us little discomforties. Well, **What problems is Databricks solving and how is that benefiting you?** In aviation, we utilize this software for data analysis. We automized a lot of processes, which simple workplace tools can not handle. We also, integrate with multiple tools (names which I can not mention for securirty purposes) Particularly, it helps us to analyze passenger demand by route and season. We combine and analyze big datasets using this software. Overall, a good tool. Out team is satisfied. **Official Response from Sara Steffen:** > Thank you for your detailed feedback. We're pleased to hear that Databricks has been instrumental in automating processes and analyzing big datasets for your aviation needs. We take your feedback about Genie and support processes seriously and are dedicated to making improvements in these areas. ### 3. Powerful Low-Latency Telemetry Pipelines with Streaming Tables & Materialized Views **Rating:** 4.0/5.0 stars **Reviewed by:** Jose P. | Head of Network Strategy, Telecommunications, Enterprise (> 1000 emp.) **Reviewed Date:** May 26, 2026 **What do you like best about Databricks?** In a telco environment handling massive data volumes from fixed and mobile networks (GPON, 4g/5g Core, and RAN) ingesting unstructured or semi-structured frequency telemetry incrementally from our virtualized functions like vEPC, vCPE or VHGW) with minimal setup. My team works closely with virtualized network functions and Multi-access Edge Computing. Features like Streaming Tables and Materialized Views help us to build low-latency pipelines that process network performance metrics near real-time, helping us monitor network KPIs and QoS efficiency. Because my team's core experties lies in network deisgn and system virtualization rather than database administration, Predictive Opimization and Liquid Clustering are highly beneficial. Tehy autonomously handle table maintenance, file compaction, and data layout optimization freeing up our resources to focus on network architecture. **What do you dislike about Databricks?** Virtualized network functions, routers, and disaggregated hardware frequently undergo software updagrades, which often introduce sublte changes in telemetry output schemas. When using structured streaming or auto loader these schema drifts cause our streaming queries to fail, requiring a manual restart of the stream to re-plan the schema. When we need to update the logic of a complex network KPI defined within a materialized view, any change to the query triggers a full recomputation of the view. Given the massive scale of telecom transaction datasets, this can result in noticeable compute costs. We rely on a variety of data tools within our ICT ecosystem, not all solutions featured in Partner Connect natively support Unity Catalog. This can crete integration and governance hurdles when we try to connect certain third-party analytics and data preperation tools to our secured data lake. **What problems is Databricks solving and how is that benefiting you?** We ingest continous streas of performance data from virualized network functions and traditional transport layers. By building streaming pipelines, we can monitor virtualized cpres and routers to identify anomalies or degredations in network traffic. Aligning with my interest in Network AI and Machine learning, our data scientists use the patform to develop predictive models. We train models on historical GPON/DSL line failures, mobile cell tower loads, and customer usage patterns to predict network congestion, schedule proactive maintenance and mitigate customer chirn across customer segments. As an evangelist for tech evolution, I use the platform to bridge the gap between our core network engineering teams and business units. By connecting business semantics and establisihng secure Delta Sharing protocols, we provide business analysts and decision makers with giverned, self service access to network insights without risking security compliance. **Official Response from Jess Darnell:** > It's fantastic to hear how Databricks is helping you ingest and process continuous streams of performance data, develop predictive models, and bridge the gap between network engineering teams and business units. We're committed to providing solutions that benefit our users in various aspects of their work. ### 4. Streamlined Data Management with a Learning Curve **Rating:** 4.0/5.0 stars **Reviewed by:** Nahid S. | Software Development Manager, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** August 04, 2026 **What do you like best about Databricks?** I really appreciate that Databricks doesn't force a trade-off between control and convenience. My developers can access the cluster configuration for detailed tuning when necessary, but for routine tasks, the managed notebooks, job scheduling, and Unity Catalog take care of everything without needing a platform specialist. I like that Databricks borrows from software engineering practices instead of sticking to the classic analytics silo. The integration with version control, CI-friendly job definitions, and separated environments mean our data work follows the same review and release processes as the rest of our codebase. This consistency makes it easier to manage as the team grows. **What do you dislike about Databricks?** I find the on-ramp steeper than it should be for people who aren't already fluent in the ecosystem. When someone on my team picks it up for the first time, there's a long stretch where they're productive with the UI but not with what's happening underneath. Spark is the main pain point. When a job degrades or fails, the reasons are rarely obvious, and tracing a problem back to a skewed join or a bad shuffle usually eats far more of a senior developer's attention than it deserves. Richer query plan visualization, with plain-language explanations of why the optimizer made a given choice, would remove a lot of that guesswork and stop these investigations from landing on the same two or three people every time. **What problems is Databricks solving and how is that benefiting you?** Databricks provides a governed data repository, making pipeline changes reviewable in pull requests. I can plan data work in sprints alongside engineering tasks, eliminating separation between data and software development. **Official Response from Jess Darnell:** > We appreciate your detailed feedback on your experience with Databricks. It's great to hear that the platform has provided a governed data repository and streamlined your data work alongside engineering tasks. We understand the challenges with the learning curve, particularly related to understanding Spark job performance and troubleshooting. We are committed to addressing these challenges and improving the onboarding process to ensure a smoother transition for new users. ### 5. Accelerated Prototyping with Seamless Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Helmi C. | IoT Solutions Architect, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** July 24, 2026 **What do you like best about Databricks?** I really appreciate how Databricks earns its place in my work as the analytics tier for the reference architectures I propose to customers. It simplifies the process by allowing telemetry from distributed device fleets to land directly, enabling me to model aggregation, anomaly detection, and historical comparison without needing separate infrastructure. The platform's cost efficiency is a decisive factor for me, and it delivers more value than its price suggests. Retrieval speed is impressive, letting me query a warehouse live during design sessions without relying on others to run the request. The setup required far less scaffolding than anticipated, positively impacting how I prototype. Starting with Databricks didn't involve the tedious configuration I expected from a platform of its scale, so the environment was productive almost immediately. Also, the platform significantly shortens the path from an idea to a prototype that clients can see, thanks to its low cost, quick retrieval, and minimal setup requirements. **What do you dislike about Databricks?** Handling of unstructured content remains the weak point in my view, since building a usable search layer over message archives, PDF documents and finance spreadsheets proved awkward enough that I compromised on the design. Vector search performance disappoints as well: direct lookups return promptly, yet reasoning queries slow noticeably, which becomes apparent the moment a client is watching a demonstration. **What problems is Databricks solving and how is that benefiting you?** I use Databricks as the analytics tier to model aggregation and anomaly detection without extra infrastructure. It allows quick data retrieval, enabling live queries during sessions, with minimal setup and low cost, transforming how I prototype and shortening the time to show customers working solutions. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has been instrumental in simplifying your analytics process and providing cost-efficient value. We appreciate your feedback around Vector Search and are constantly working to improve our platform. ### 6. Unified, Scalable Databricks Platform for Collaborative Data Engineering and ML **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Internet | Mid-Market (51-1000 emp.) **Reviewed Date:** July 17, 2026 **What do you like best about Databricks?** What I like most about Databricks is its unified platform for data engineering, analytics, and machine learning. It makes working with large datasets much easier and allows different teams to collaborate in a single environment. I especially like the notebook-based workspace, its scalability, and its integration with Apache Spark. **What do you dislike about Databricks?** What I dislike about Databircks is that the platform can come with a learning curve for new users, especially when you’re dealing with advanced configurations and large-scale data pipelines. Managing compute resources and keeping costs under control can also take careful monitoring and ongoing attention. **What problems is Databricks solving and how is that benefiting you?** It helps solve the challenge of managing, processing, and analyzing large volumes of data across different workflows. It brings data engineering, analytics, and machine learning into a single platform, which reduces the need to switch between multiple tools and makes it easier to keep work consistent from end to end. **Official Response from Jess Darnell:** > We're glad to hear that you appreciate the unified platform and collaboration features of Databricks. Our notebook-based workspace, scalability, and integration with Apache Spark are designed to make working with large datasets easier for teams. Thank you for sharing your positive experience! ### 7. Seamless, Collaborative Platform That Scales for Data Engineering and ML **Rating:** 4.0/5.0 stars **Reviewed by:** Krish G. | student, Small-Business (50 or fewer emp.) **Reviewed Date:** April 15, 2026 **What do you like best about Databricks?** Databricks' ability to seamlessly integrate everything is what I find most appealing. When working on actual projects, it really makes a big difference that you don't have to switch between several tools for data engineering, analysis, and machine learning. The collaborative element is very noteworthy. Teams may easily collaborate without things becoming messy thanks to the notebooks' fluid and dynamic feel. For significant data work, it resembles Google Docs almost exactly. I also really like how efficiently it manages large amounts of data without making it seem difficult. Even when working with large datasets, the platform feels user-friendly and can be scaled up when necessary. Additionally, it makes perfect sense from an AI/ML standpoint. You are able to construct, **What do you dislike about Databricks?** Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together. Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things. Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together. Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things. **What problems is Databricks solving and how is that benefiting you?** The fragmentation issue in the data and AI workflow is primarily resolved by Databricks. In the past, data storage, processing, analysis, and machine learning were usually done using different tools, and getting them all to cooperate was frequently difficult and time-consuming. Databricks eliminates a lot of the friction by combining all of it into a single platform. That makes the developing process much more seamless for me. I don't have to worry about compatibility problems or waste time switching between environments. I can perform transformations, clean data, and create models all in one location, which reduces setup time and maintains organization. It also addresses the difficulty of handling massive amounts of data. I can rely on its distributed computing capabilities to manage demanding workloads rather than worrying about infrastructure or performance optimization from scratch. This allows me to concentrate less on resource management and more on finding a solution to the real issue. Collaboration is another major issue it resolves. Sharing code, findings, and experiments can get disorganized in team environments. Because everything is consolidated with Databricks, it's simpler to work together, monitor changes, and maintain alignment. All things considered, it helps me by cutting down on complexity, saving time, and allowing me to concentrate more on developing solutions—whether they be analytics, machine learning models, or data pipelines—instead of handling the overhead of maintaining numerous tools and platforms. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' seamless integration and collaborative features appealing. We understand that the platform may feel overwhelming initially, but we offer comprehensive resources and support to help users get up to speed. Regarding cost control, we recommend leveraging our documentation and best practices to optimize cluster usage and auto-scaling settings. Your feedback is appreciated and we are committed to continuously improving the user experience! ### 8. Unified Databricks Workspace That Streamlines Collaboration and Complex Data Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Neeraj Kumar N. | AI Data Specialist | Transcription & Annotation Expert | AI Model Training at Sigma AI, Mid-Market (51-1000 emp.) **Reviewed Date:** April 12, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning into one unified workspace. I find collaboration much easier with shared notebooks, and the seamless integration with big data tools saves me time. It simplifies complex workflows while still offering powerful capabilities when I need them. **What do you dislike about Databricks?** One thing I dislike about Databricks is that it can feel expensive, especially for smaller projects or teams. I also find cluster configuration and cost management a bit complex at times. The interface, while powerful, can be overwhelming for beginners, and debugging distributed jobs isn’t always as straightforward as I’d like. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of handling large-scale data processing, analytics, and machine learning in one place. For me, it removes the hassle of managing separate tools and infrastructure. I benefit by working more efficiently, collaborating easily with my team, and turning complex data into useful insights faster, with less operational overhead overall. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' unified workspace and collaboration features valuable for your work. We understand your concerns about cost and complexity, and we're continuously working to improve in these areas. ### 9. Databricks Lakehouse Powerhouse with Unity Catalog and Fast Photon SQL **Rating:** 4.0/5.0 stars **Reviewed by:** Vidhyadar R. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** I really value how the platform brings data lakes and warehouses together into one place. It makes managing data much easier, and the SQL performance is very fast thanks to the Photon engine. I also like the collaborative notebooks because they allow me to work with both SQL and Python seamlessly in a single environment. **What do you dislike about Databricks?** The cost can be high, and the DBU billing system is quite complex to track. I also found that there is a significant learning curve when it comes to Spark and configuring clusters. For smaller, quick tasks, the setup time and technical overhead can sometimes feel like a bit too much. **What problems is Databricks solving and how is that benefiting you?** ​It solves the issue of having data scattered everywhere. I love that I can switch between SQL and Python in the same spot, and the processing speed is top-notch. It’s been a game-changer for building out our financial models quickly without the usual lag. **Official Response from Janelle Glover:** > We appreciate your feedback on the benefits of Databricks, such as the centralized data management and the ability to work with SQL and Python in a single environment. We understand your concerns about cost and the learning curve, and we're actively working to enhance the platform to better meet your needs. ### 10. The most advanced integrated single data platform **Rating:** 4.0/5.0 stars **Reviewed by:** Vitor S. | Senior Data Consultant, Marketing and Advertising, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** De integration and ability to work with data under very different aspects, influences under a single unified platform, the performance and how easy is to connect to complex data sites and organize data. Extracting value from the data is also one of the things that I love Data Bricks made possible that huge volumes of data that companies that didn’t have the technology to achieve could handle this data in a very short period reducing complexity, and the data democratization **What do you dislike about Databricks?** The platform is becoming very crowded with features and products, and that sometimes are not yet fully operational or fully integrated. There are limitations in the newer products that Mia’s customer expected that they they would be they would have same features as previous product and capacity to customize and govern things in an easier way for instance, unity catalogues is too very complex to deploy integrate and it’s not in embedded or native to other product integrations **What problems is Databricks solving and how is that benefiting you?** Data Bricks is making impossible my company to integrate data sets and reduce business complexity **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the integration and data management capabilities of Databricks. We understand your concerns about the increasing number of features and products, and we are continuously working to improve their integration and operational functionality. Your feedback is valuable to us as we strive to provide a seamless and efficient platform for our users. ### 11. Efficient and User-Friendly, But Needs Better BI Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Anshu D. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like the scalability aspect of Databricks, where I can tailor the back end based on the data volume I'm working with, and even right size it if the volume isn't large. The efficiency and the speed of the product also stand out to me, speaking volumes about what it offers. Additionally, Databricks' user-friendly interface makes coding and managing errors easier. The installation from a package standpoint is relatively straightforward and easy to use, which helps me understand what I need to do. **What do you dislike about Databricks?** I think I'd like to learn a little bit more about the BI capabilities. In terms of once you run your production pipeline or process, how can I initiate directly sort of a report? That can give me more analytics in terms of the quality of the data and also trending of certain key elements that are key or integral to the data that we are trying to create. **What problems is Databricks solving and how is that benefiting you?** Databricks provides a user-friendly interface, making coding easy and errors straightforward to fix. The software installation is simple. It enhances coding efficiency and the speed of data processing. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks scalable and efficient, with a user-friendly interface. We appreciate your feedback on the BI integration and will take it into consideration for future improvements. ### 12. Good Overall Experience, but the Cost Is a Concern **Rating:** 3.5/5.0 stars **Reviewed by:** ergin y. | IT Specialist - Intern, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** The way Databricks structures data processing into the Medallion Architecture (Bronze \rightarrow Silver \rightarrow Gold) provides a clean, repeatable blueprint for building data pipelines: Bronze (Raw): Ingests raw data directly from source systems without modification, acting as the historical ledger. Silver (Enriched/Cleaned): Cleans, filters, conforms, and enriches the data. This acts as the single source of truth for enterprise-level reporting. Gold (Curated/Aggregated): Aggregates data into business-level metrics optimized for specific analytics, BI dashboards, or machine learning models. **What do you dislike about Databricks?** Cost management.. Because Databricks separates compute from storage, it scales horizontally with massive power—but that power can quickly become a financial liability if not governed tightly. The DBU Engine: Databricks charges in Databricks Units (DBUs) on top of the underlying cloud provider's infrastructure costs (like AWS or Azure VMs). Idle Cluster Drain: If auto-scaling or auto-termination settings aren't configured perfectly, an idle cluster left running can rack up thousands of dollars overnight. Keeping a tight grip on cost management requires a lot of proactive monitoring and custom alerting. **What problems is Databricks solving and how is that benefiting you?** At its core, Databricks is solving one of the most persistent bottlenecks in modern enterprise technology: data fragmentation. **Official Response from Janelle Glover:** > We're glad to hear that Databricks is helping your business address data fragmentation. We understand your concern about cost management and are continuously working to provide more transparent and efficient pricing options. ### 13. 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. ### 14. 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. ### 15. 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. ### 16. 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. ### 17. 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. ### 18. 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. ### 19. 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. ### 20. Easy API Data Pulls and Collection Management, Plus AI-Powered Coding **Rating:** 4.0/5.0 stars **Reviewed by:** Reetika P. | Quality engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** July 28, 2026 **What do you like best about Databricks?** It easily pulls data from the API, and within the same dataset we can manage our collections. We also have the option to write code using the AI. **What do you dislike about Databricks?** In our current setup, BigQuery SQL queries run with predictable costs that are easy to control. With Databricks, though, if a data engineer spins up an oversized cluster or leaves a node running after processing dealer posts or telematics logs, compute costs can ramp up quickly and may go unnoticed. **What problems is Databricks solving and how is that benefiting you?** For our projects, we use it to pull the source, or raw, data from the APIs and then transfer that same data into BigQuery. It essentially acts as a middleman for us. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks useful for pulling data from APIs and managing collections within the same dataset. The AI-powered coding feature is also a great benefit. We understand your concerns about potential cost escalation with Databricks. We recommend closely monitoring cluster usage and setting up alerts to avoid unexpected compute costs. ### 21. Unified Analytics Powerhouse with Minor Hiccups **Rating:** 4.0/5.0 stars **Reviewed by:** mohammad Gufran j. | Senior Associate Engineer (Azure Platform and Databrick Engineer), Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 25, 2026 **What do you like best about Databricks?** I like most about Databricks is that it brings data engineering, analytics, and AI workflow into one shared platform, which makes collaboration much easier. It's valuable for working with a large dataset and notebooks, and it helps set up suitable pipelines without the hassle of managing too many separate tools. **What do you dislike about Databricks?** Cost visibility and resource users can be hard to track, especially as more teams cluster and jobs start using the platform. I also like to sync up permission management. Clear troubleshooting for a job failure and a smoother experience around the workspace governance and configuration. CDC lake flow is always stuck for a last table and not giving a clear picture till now. Serverless logs are sometimes very difficult to track, making it hard to understand the reason for job failures. **What problems is Databricks solving and how is that benefiting you?** I find Databricks solves handling large-scale data processing and analytics by unifying data engineering, analytics, and AI workflow into one platform. It simplifies collaboration on notebooks and automation workflows, enabling faster work with big datasets using Spark. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks valuable for unifying data engineering, analytics, and AI workflow into one platform, making collaboration easier and simplifying big data processing. ### 22. Unified Platform with Powerful Features, Needs Faster Cluster Startups **Rating:** 4.0/5.0 stars **Reviewed by:** Yash P. | Software Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 23, 2026 **What do you like best about Databricks?** I appreciate how Databricks brought everything onto one unified platform, allowing our teams to collaborate in shared notebooks and ensuring data consistency with Delta Lake's ACID transactions. My favorite feature is Auto Loader, which automatically ingests new data files as they land in cloud storage, saving our team 2-3 hours a week on manual pipeline monitoring. Unity Catalog has been a game changer for us, providing a central place for governance and access control, which before was a mess. The initial setup was straightforward, and we had our first cluster and notebooks connected to S3 within a day, which was impressive given the platform's power. The workspace configuration and cloud integration guides are solid to follow. **What do you dislike about Databricks?** The cluster startup time is something that still catches us off guard. Cold start can take anywhere from 3-5 minutes, which gets frustrating when you are in the middle of an iterative debugging session and just need to test a quick fix. The cost management also needs some upgrades as currently the billing dashboards are improving but it still takes some digging to pinpoint exactly which job or user is driving up spend. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to unify our data processing and machine learning, reducing pipeline delivery delays by 40%. It enables team collaboration with consistent data, saving hours with the autoloader, and simplifies governance with Unity Catalog. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the unified platform and powerful features of Databricks, such as the Auto Loader and Unity Catalog. We understand your frustration with the cluster startup time and cost management, and we are continuously working to improve these aspects to provide a better user experience. ### 23. Powerful platform for Data analytics **Rating:** 4.0/5.0 stars **Reviewed by:** Aditya Y. | Student, Small-Business (50 or fewer emp.) **Reviewed Date:** June 01, 2026 **What do you like best about Databricks?** For me and my team Databricks brings data engineering as well as analytics and machine learning into one platform . Genuinely speaking it makes our work easy with large datasets . their pricing is also good **What do you dislike about Databricks?** Honestly saying some advanced features of Databricks can be difficult to configure initially or simply I should say the initial setup can be complex for beginners **What problems is Databricks solving and how is that benefiting you?** For our team it helps manage and analyze large datasets efficiently I must say It improves collaboration between teams and makes it easier to generate insights for business decisions . **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been a powerful platform for your data analytics needs, combining data engineering, analytics, and machine learning in one place. We appreciate your feedback on the pricing as well. ### 24. Streamlined Data Architecture & AI Solutions **Rating:** 4.0/5.0 stars **Reviewed by:** Siddhesh S. | Business Intelligence Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 12, 2024 **What do you like best about Databricks?** I love how easy it is to integrate multiple data workloads such as Data Warehouse, Data Lakes, and Model Registry for the organization in a single interface with Databricks. **What do you dislike about Databricks?** I think working with Catalog still requires a different tab open. If we can integrate it into the multiple tabs section within Databricks UI, it would reduce context switching and would help users to stay focused and productive. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to create workflows for data architecture and AI solutions, solving the problem of maintaining multiple workloads. It's easy to integrate data warehouses, lakes, and registry in one interface. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with the Databricks Data Intelligence Platform. We understand the frustration with updating single tables from multiple threads and will work to address this in future updates. ### 25. Efficient Setup, Needs Better Unstructured Data Support **Rating:** 4.0/5.0 stars **Reviewed by:** Leo W. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like Databricks because it's cost-effective and makes it easy to build agents. The data retrieval is quick and agent building is straightforward. The setup process is not challenging, making it easy to get started without any hassles. **What do you dislike about Databricks?** I find the support for unstructured data lacking. It's not easy to build a search engine based on unstructured data like email content, policy PDF documents, and finance Excel files. I also have concerns about the query performance. The vector search is pretty slow, especially with reasoning questions, as opposed to lookup questions where the performance is good. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for quick data retrieving and easy agent building for our data warehouse, helping provide insights to gain more customers. **Official Response from Jess Darnell:** > It's great to hear that Databricks is helping you with quick data retrieval and easy agent building for your data warehouse, ultimately providing insights to gain more customers. Thank you for your feedback on the support for unstructured data. We are constantly working to improve our platform and will take your concerns into consideration. ### 26. Seamless Integration and Scalable Performance with Room for UI Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Ashley F. | Senior Executive, Small-Business (50 or fewer emp.) **Reviewed Date:** April 20, 2026 **What do you like best about Databricks?** I use Databricks to build ETL pipelines and process large-scale data with Spark. I like Databricks most for its seamless integration with Apache Spark, collaborative notebooks, and its ability to handle large-scale data processing efficiently in a unified platform. The seamless Apache Spark integration lets me process huge datasets quickly without worrying about cluster setup, while collaborative notebooks make it easy to work with my team in real-time. The scalable architecture ensures reliable performance even with heavy data workloads. The initial setup of Databricks was fairly straightforward, especially with cloud integration. **What do you dislike about Databricks?** The UI can feel a bit cluttered at times, cluster startup times can be slow, and the pricing can get expensive for smaller projects or prolonged usage. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to efficiently process large-scale data, simplify ETL workflows, and collaborate with my team in a unified environment, gaining faster data-driven insights. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the seamless integration with Apache Spark and the collaborative features of Databricks. We appreciate your feedback on the UI and cluster startup times, and we are continuously working to improve these areas. Regarding pricing, we offer various options to accommodate different project sizes and usage durations. Thank you for sharing your experience with us! ### 27. BI and Data Engineering in One Place, with AI-Assistant **Rating:** 4.0/5.0 stars **Reviewed by:** Corrado P. | Service Designer and Workshop Facilitator, Enterprise (> 1000 emp.) **Reviewed Date:** April 23, 2026 **What do you like best about Databricks?** Possibility to combine data warehousing and data lakes into a “lakehouse.” So I can do BI and data engineering all in one place instead of stitching together multiple systems. Using AI to improve and make faster the SQL writing and execution **What do you dislike about Databricks?** Unity Catalog is powerful, but setting up fine-grained access control across data, schemas, and workspaces can become tricky, especially in larger organizations. The UX/UI of some parts of the platform feels polished, others less so. **What problems is Databricks solving and how is that benefiting you?** Databricks is essentially solving fragmentation and inefficiency across the data lifecycle and the benefits come from removing a lot of friction between teams, tools, systems and data. **Official Response from Jess Darnell:** > It's great to hear that Databricks is solving fragmentation and inefficiency across the data lifecycle for you, and that it's removing friction between teams, tools, systems, and data. ### 28. Databricks Streamlined Our ETL Migration with Delta Lake and Unified Analytics **Rating:** 3.5/5.0 stars **Reviewed by:** Yuvi M. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** Databricks transformed my day-to-day workflow, taking me from constant SQL Server/ADF headaches to scalable, unified analytics. Migrating stored procedures into Spark SQL notebooks was surprisingly smooth, and using Delta Lake MERGE instead of complicated UPDATE logic saved me weeks of rewriting. The most helpful features for me have been Delta Lake’s ACID transactions and schema evolution, which handle my sparse shipment loads really well. Unity Catalog has also been a big win because it eliminates the back-and-forth of RDS access tickets by enabling governed table sharing. On top of that, Genie turns natural-language requests into production-ready Spark SQL almost instantly. On the upside, autoscaling clusters have cut costs by about 70% compared with ADF’s always-on pipelines. I also like being able to combine PySpark and SQL in a single notebook, which makes complex joins and subqueries much easier to manage. And I don’t miss the old NOLOCK hint debates—built-in optimizations take care of that. If you’re migrating ETL pipelines, Databricks removes a lot of the SQL-to-cloud friction while still scaling to enterprise volumes without breaking the bank. **What do you dislike about Databricks?** The cluster reconnects fairly often, which can be disruptive during active work sessions. Also, when I run complex or heavy queries, I notice clear lag in response times, and that slowdown can hurt productivity. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us centralize our data engineering and analytics workflows into a single, unified platform. It addresses the challenge of managing large-scale data pipelines by enabling our team to process and transform massive datasets efficiently with Spark. The collaborative notebook environment has also boosted productivity, making it easier for data engineers and analysts to work together. Overall, it has significantly reduced the time we spend on data preparation and has allowed us to focus more on deriving insights. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks, especially regarding the smooth migration process, cost savings with autoscaling clusters, and your experience with Genie and Unity Catalog. We understand your concerns about cluster reconnects and query response times, and we're actively working to enhance the platform's performance for all users. ### 29. Databricks Streamlines Data Workflows with Powerful, Scalable Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Transportation/Trucking/Railroad | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks is a great platform that makes it easy to work with large amounts of data. It provides a user-friendly experience and offers powerful tools for data engineering, analytics, and machine learning. The platform is reliable, scalable, and helps teams collaborate effectively. Overall, Databricks has been a valuable solution for improving productivity and streamlining data workflows. **What do you dislike about Databricks?** One downside of Databricks is that it can be expensive, especially as usage grows. Some advanced features may also have a learning curve for new users. Additionally, managing costs and understanding the pricing structure can sometimes be challenging. However, the overall capabilities and performance of the platform generally outweigh these drawbacks. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to work with data and run analytics workloads more efficiently. The platform helps simplify data management and provides a scalable environment for processing large datasets. It has helped improve workflow efficiency, collaboration, and overall productivity while making it easier to access and analyze data. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been valuable in streamlining your data workflows and improving productivity. Our platform is designed to provide a user-friendly experience and powerful tools for data engineering, analytics, and machine learning. ### 30. Databricks’ Unified Platform Simplifies Scalable Data Pipelines and Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Banking | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is its unified platform that brings together data engineering, analytics, AI, and governance in a single environment. It simplifies building reliable data pipelines, supports scalable processing with Spark and Delta Lake, and enables teams to collaborate more effectively across the entire data lifecycle. **What do you dislike about Databricks?** The quality and depth of some training and learning materials could be improved. In some cases, the content feels too high-level and would benefit from more real-world examples, hands-on exercises, and deeper technical explanations for advanced users. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of managing large-scale data processing, analytics, and AI workloads across multiple tools and platforms. By providing a unified environment for data engineering, data warehousing, governance, and machine learning, it reduces complexity and improves productivity. For me, this means I can build and maintain data pipelines more efficiently, ensure data reliability with Delta Lake, and spend more time delivering business value instead of managing infrastructure. **Official Response from Aunalisa Arellano:** > We're glad to hear that you appreciate the unified platform and its ability to simplify data pipelines and collaboration. We're continuously working to improve our training and learning materials to provide more practical and in-depth content for all users. Thank you for the feedback and for taking the time to leave a review! ### 31. An all-in-one platform **Rating:** 4.0/5.0 stars **Reviewed by:** Pang L. | Machine Learning Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 18, 2026 **What do you like best about Databricks?** It's an all-in-one platform for data engineers, analysts, data scientists, and business users. **What do you dislike about Databricks?** It’s easy to overspend and it is a vendor lock-in. **What problems is Databricks solving and how is that benefiting you?** Data engineering, model training and inference, GenAI. Databricks solves the problem of having fragmented tools across the data and AI lifecycle. Traditionally, teams would need separate platforms for data engineering, analytics, machine learning, and AI — leading to silos, duplicated work, and governance challenges. With Databricks, data engineering pipelines, model training and inference, and GenAI development all live in one unified environment. This means data engineers can build and orchestrate pipelines, data scientists can train and deploy models, and teams can develop and serve GenAI applications — without constantly moving data or context-switching between tools. **Official Response from Janelle Glover:** > Thank you for sharing the specific problems that Databricks is solving for you. We are committed to streamlining the data and AI lifecycle to eliminate silos, duplicated work, and governance challenges. ### 32. Versatile Data Platform with Seamless Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Ayobami A. | Senior Consultant - BI, Data & Analytics, Enterprise (> 1000 emp.) **Reviewed Date:** March 28, 2026 **What do you like best about Databricks?** What I like most about Databricks is that it's integratable with other platforms. I can literally set up a Databricks workspace using Azure data services from the Azure portal, and I can also use Databricks within AWS. It gives me the opportunity to integrate my Databricks notebooks into other environments and orchestration tools or ETL tools, like Azure Data Factory. **What do you dislike about Databricks?** For now, I noticed when I'm using Azure Databricks, particularly the Azure Databricks cluster, it usually times out, and it's kind of frustrating for me. Most times when I'm working, I just go into another tab. Every time I come back in a minute or two, it's timed out, and I have to sign in again. That experience can be frustrating. I would like that to be looked into. I don't know if it's an issue with Databricks or if it's an issue from the Azure side from the intraident authentication part of things. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to unify my data by managing governance within the Unity catalog, simplifying user access and report sharing. **Official Response from Janelle Glover:** > It's fantastic to hear that Databricks is helping you unify your data, manage governance within the Unity catalog, and simplify user access and report sharing. We apologize for the frustration you've experienced with the Azure Databricks cluster timing out. Can you please contact our team via www.databricks.com/support so we can help look into this further? Thank you! ### 33. Comprehensive Platform with Room for Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Sathya R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** I find Databricks to be a one-stop solution because it incorporates various functionalities such as orchestrating pipelines. It also has an inbuilt AI called Genie, which helps in building jobs, and other AI-related tasks. I appreciate that compared to other providers like AWS and Azure, Databricks offers specific features that they lack, allowing me to use the database simply and access everything in one place. The initial setup was quite easy because I could use a single stop to directly implement and update tables using the data lakehouse, which is easier compared to others **What do you dislike about Databricks?** I think Databricks could improve on the orchestration part. Even though it has orchestration capabilities for pipelines and jobs, it misses the ease of access that something like Airflow provides, which is specifically designed for orchestration. It would be helpful if Databricks adopted a pattern similar to Airflow's for better orchestration and job linking. I also feel the Genie part could be improved. While the Genie works well, the output duration can be lengthy, usually taking more than five to ten minutes to perform specific tasks. So, I would like to see improvements in that area as well. **What problems is Databricks solving and how is that benefiting you?** I use Databricks as a one-stop solution for various tasks. It orchestrates pipelines and utilizes an inbuilt AI, making it more feature-rich than alternatives like AWS or Azure. This allows me to streamline workflows without relying on multiple providers. **Official Response from Janelle Glover:** > Thank you for sharing your feedback on Databricks. We're glad to hear that you find our platform to be a comprehensive solution with valuable functionalities such as Genie. We appreciate your input on areas for improvement and will take your suggestions into consideration for future updates. ### 34. Seamless Integration, Needs Performance Tuning **Rating:** 4.0/5.0 stars **Reviewed by:** Pandi A. | Lead Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** I think the most useful part of Databricks is its single architecture where you can have everything, like a database and dashboard, all in one. Compared to other providers like Azure or AWS, where I would need multiple services, Databricks offers everything in a single service. This simplifies my work because I don't have to manage integration or network level details across different services. The convenience of having everything inside Databricks means I can avoid multiple network updates when connecting with tools like Power BI, which makes it a standout feature for me. Additionally, the initial setup after migrating from Snowflake was pretty easy since Databricks allows us to manage access and security within a single service. **What do you dislike about Databricks?** Yeah, so one thing that needs to be updated is Genie code. If I look at it, Genie code is helpful for generating code but when it does in the back end, it consumes much memory. For example, if I'm opening Databricks in Chrome, it's gonna take at least one or two GB memory at the back end, and that takes a lot of time to generate the response as well. So if we could reduce that, it would be great. Also, on the pipeline stuff, for example, if you take Airflow, Airflow is specifically designed for our position. We use Airflow and I can see, for example, if I have thousands of jobs, I can see each and every job and what's happening. But with Databricks, it's a tough job for me to see the success and failures and to manage the charts. We have multiple options to monitor it in Databricks, but it's hard when compared with Airflow. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us consolidate data from different locations into a single database, simplifying master data management and making data access easier with integrated dashboards, improving our AI-powered sales and prospect tracking. **Official Response from Janelle Glover:** > Thank you for taking the time to provide your feedback. We're pleased to hear that Databricks has helped simplify your data management and improve your AI-powered sales and prospect tracking. We have noted your feedback on Genie and pipeline management, and we will explore ways to enhance these features for a better user experience. ### 35. Simplifies Data Engineering, Needs Better Tool Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Aladdin A. | Senior Solutions Engineer - Cloud & AI Data, Mid-Market (51-1000 emp.) **Reviewed Date:** March 26, 2026 **What do you like best about Databricks?** I like the features of Genie, especially the new junior code, which makes it possible to get SQL-ready scripts by just chatting using natural language. This is fascinating, especially with the governance layer on top of it with Unity. It accelerates both analysts' and engineers' jobs by helping build reports and getting them ready efficiently, especially since it has access to most of the metadata. The documentation is also useful, suggesting SQL code that can be provisioned on the fly. Tying Genie with AI functions such as the ai_query makes it a superpower. **What do you dislike about Databricks?** Honestly, a ton of features that can be improved, especially connectivity with other tools, such as cloud tools, especially like Azure. As a Microsoft employee, I evangelize Databricks, but many of our clients use the Microsoft stacks extensively. Sometimes, these tools feel isolated from the whole stack. There’s still a lot of work to be done to connect models provisioned in Azure and things like unity catalogs or governance that can sit outside of Databricks and Microsoft's stack. This feels like a disconnect, especially in highly regulated environments where on-prem stuff needs to interact with Databricks capabilities. **What problems is Databricks solving and how is that benefiting you?** Databricks simplifies provisioning services, streamlines data engineering, and speeds up workflow creation. It combines tools into one governed platform, making handling big data easier and faster. Its AI layer integrates well, reducing the need for multiple tools. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the features of Genie. We understand your concerns about the connectivity with other tools, especially Azure, and we are continuously working to improve integrations to provide a seamless experience across different platforms. ### 36. Outstanding Experience with This Software **Rating:** 4.0/5.0 stars **Reviewed by:** Kriti K. | CFO, Mid-Market (51-1000 emp.) **Reviewed Date:** January 09, 2026 **What do you like best about Databricks?** Databricks data intelligence is a platform that helps in accommodating all of our business and official data and share it with different team departments so that they can analyse it and create a detailed analytics of past performances and also make required changes on it for future growth. **What do you dislike about Databricks?** One of the major challenge that we face while working with Databricks data intelligence platform is that you cannot use this tool with a single data scientist you will have to keep a team of professionals who can deal with large data and create multiple graphs and analytics according to available information and this complete activity involves lot of financial investment **What problems is Databricks solving and how is that benefiting you?** This software help us in making sure that all the data of different departments are accommodated in a same software so that access can be easier and decisions can be taken much quicker. With the help of this tool data of all the departments like finance, operations, sales and marketing are screend in one time and thoroughly interchecked too. **Official Response from Janelle Glover:** > It's fantastic to hear how Databricks Data Intelligence Platform is benefiting your business by streamlining access to data from different departments and facilitating quicker decision-making. We appreciate your feedback on the financial investment and will take it into consideration as we continue to improve user experience and ensure our platform is accessible for all users. ### 37. Lakebase is great but needs better monitoring & auditing **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Health, Wellness and Fitness | Enterprise (> 1000 emp.) **Reviewed Date:** March 24, 2026 **What do you like best about Databricks?** Bidirectional Sync capabilities in Databricks Lakebase & Lakehouse allows us to establish continuous healthcare intelligence where analytics in Lakehouse and operations in OLTP Lakebase remain tightly connected. We invested heavily in the modern Lakehouse architectures for enterprise data analytics AI and discovery. Clinical systems and patient interaction data flow into governed Databricks Lake houses where we build Care 360 views and healthcare key insights. **What do you dislike about Databricks?** Setting up Sync process requires extensive curation and planning to achieve great outcomes. Cost management is a big concern when multiple teams as multiple departments in my org have various policies. The records are dropped if the PK is null for data in lake houses. There's room for improvement to better handle it in future **What problems is Databricks solving and how is that benefiting you?** Databricks solves ingestion, transformation, governance, and data quality challenges, offering AI and BI tools for instant insights. Bidirectional Data sync (Lakehouse -> Lakebase) processes can operate together as a unified architectural pattern in the Enterprise Healthcare Intelligence Platform. Healthcare enterprises have historically struggled because analytical systems and operational systems evolved independently. Bidirectional Sync introduces a unified model where Analytics, Operational serving, AI activation & Continuous learning coexist within the same governed ecosystem that significantly reduces Data duplication, Pipeline sprawl & Synchronization complexity while improving Operational intelligence, Data freshness, Auditability & Decision latency. Forward Sync operationalizes healthcare intelligence. Reverse Sync reactivates operational knowledge back into the learning system. Together, they establish a continuous intelligence architecture where every healthcare interaction contributes toward improving patient outcomes, operational efficiency and AI effectiveness at enterprise scale. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Genie and Databricks. We appreciate your feedback on setting up Genie. We are continuously working to enhance our semantic model and cost management features for a better user experience. ### 38. Databricks Makes Collaboration and Reliable Data Pipelines Easy **Rating:** 4.0/5.0 stars **Reviewed by:** Raja B. | Senior Solutions Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 28, 2026 **What do you like best about Databricks?** I really enjoy working in the Databricks environment because it makes it easy to collaborate with others through shared notebooks. Delta Lake technology has also been great for ensuring data quality and reliability across our pipelines. It lets us manage data, build pipelines, and run AI/BI workloads all in one place. **What do you dislike about Databricks?** The interface is quite laggy at times, especially when I’m scrolling through a notebook or spinning up a cluster. **What problems is Databricks solving and how is that benefiting you?** Because it’s a unified, end-to-end platform covering everything from data ingestion and transformation to AI and BI insights, it enables faster analysis and helps convert complex datasets into actionable decisions more efficiently **Official Response from Janelle Glover:** > We're thrilled that Databricks has been beneficial for your data pipelines and analysis. We're sorry to hear that you have experienced lags in the interface. We appreciate your feedback on this, and our team will work to address this. ### 39. Enhanced Data Management with Some Connectivity Challenges **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really love using Databricks, especially for the Unity Catalog. Genie is a real help when it comes to database tasks and connecting to APIs or writing code, correcting mistakes and solving issues. The AI aspect of it is fun and quite beneficial to my workflow. **What do you dislike about Databricks?** The major improvement that I look forward to in Databricks is how all these, whether it's data lakehouse, Genie two, all these areas, how it will connect. Providing more guidance with respect to training is where I'm looking forward to that kind of improvement. One of the major difficulties is while connecting to serverless, we find it's losing the connectivity. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the major challenge of understanding complex data by providing layers to analyze it well and aids in connecting to APIs or writing code with fewer mistakes, thanks to Genie. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks, particularly the benefits of using Genie. We acknowledge the connectivity challenges you've faced and are committed to enhancing the platform's connectivity, including providing more guidance for training. Your feedback is valuable to us. ### 40. Effortless Data Analytics and App Building **Rating:** 4.0/5.0 stars **Reviewed by:** Moksha D. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for data analytics, building agents and apps. It makes analytics easy and quick, and helps maintain data and control versions easily. I really enjoy the app and agent building capabilities, as building apps used to require much effort, but now it’s easier with Databricks. Additionally, the initial setup was smooth. **What do you dislike about Databricks?** Agent handling things could be improved. We create agents through UI, which makes it easy to create but doesn’t give you full control on handling it. **What problems is Databricks solving and how is that benefiting you?** I find Databricks makes analytics easy and quick, helps maintain data and control versions easily, and simplifies app building significantly. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has simplified your data analytics and app building processes. We value your input on agent handling and will use it to enhance the user experience. Thank you for choosing Databricks. ### 41. Efficient Data Management with Intuitive Interface **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for querying my production data from S3 and for development and debugging using production data. I like how easy it is to navigate the platform and find what I need. I appreciate that Databricks allows me to run new code against production data in a safe way. I like the notebooks, which allow me to work adhoc. I also like the Unity Catalog for reviewing my data lake. It's easy to get started on. **What do you dislike about Databricks?** I feel like I don’t know enough about all of Databricks' utility, thus I only use a fraction of it. If it had prompts that could show me useful features, like tool tips. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to query and develop with production data from S3. It lets me safely run new code against prod data, which isn't otherwise possible. **Official Response from Aunalisa Arellano:** > We appreciate your positive feedback about Databricks' intuitive interface and its benefits for querying and developing with production data. We understand the need for better guidance on utilizing the full range of Databricks' features, and we're committed to improving the platform to provide more helpful prompts and tooltips for users. We also encourage you to check out the Databricks Academy if you haven't yet. It is an excellent resource to learn all about the product. ### 42. Streamlined Data Management and Transformation **Rating:** 4.0/5.0 stars **Reviewed by:** Puttaraju D. | Associate, Mid-Market (51-1000 emp.) **Reviewed Date:** May 14, 2026 **What do you like best about Databricks?** I use Databricks for storing and consuming data. I really like the unified catalog feature, as it helps me manage permissions and access to metadata easily. The ability to publish datamart data to Thoughtspot is beneficial, and I find data transformation using Databricks notebooks particularly helpful. The ease of initial setup with Databricks was great and our team of over 1000 people transitioned smoothly from Hadoop. **What do you dislike about Databricks?** Table level access. Provision to restrict access at table level is required. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for storing and consuming data, with a unified catalog for easy access to metadata. The ability to transform and publish datamart data to Thoughtspot is valuable, though I'd like improved table-level access restrictions. **Official Response from Jess Darnell:** > Thank you for sharing your positive experience with Databricks, especially regarding the unified catalog feature and the ease of initial setup. We understand your concern about table-level access and will take that into account for future enhancements. ### 43. Unified Data Platform That Simplifies Complex Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** PRATYUSH A. | Student and researcher, Small-Business (50 or fewer emp.) **Reviewed Date:** December 13, 2025 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning together on one platform. Having a unified environment built around Apache Spark makes collaboration between data teams much easier. The Lakehouse approach works well because it removes the need to move data across multiple tools. Performance is strong for large datasets, and notebooks make experimentation, analysis, and collaboration more efficient. Overall, it simplifies complex data workflows while still being powerful. **What do you dislike about Databricks?** The main downside is the cost and complexity for new users. Pricing can be hard to predict, especially when workloads scale unexpectedly, and compute costs can rise quickly if not monitored closely. There is also a learning curve for teams that are not already familiar with Spark or cloud-based data platforms. Some advanced configurations and optimizations require experienced resources, which can slow adoption for smaller or less mature data teams. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us solve the problem of working with large, fragmented datasets across different tools and teams. Earlier, data engineering, analytics, and machine learning were handled in separate systems, which created silos and slowed down insights. With Databricks, we can process, analyze, and model data on a single platform, which improves collaboration and reduces data movement. From a business perspective, it helps us generate insights faster, scale analytics as data grows, and improve data reliability. This leads to quicker decision-making, more consistent reporting, and better use of data for forecasting and optimization, while reducing operational overhead. **Official Response from Janelle Glover:** > It's great to hear that Databricks has helped streamline your data workflows and improve collaboration across teams. We recognize the challenges around cost, complexity, and the learning curve, and we are dedicated to addressing these concerns to ensure a more seamless experience for all users. Thank you for your valuable feedback. ### 44. Simplifies Data Analysis for Streamlined Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Pragati V. | Social Media Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** January 06, 2026 **What do you like best about Databricks?** Databricks is a Data Analytics tool that is used by our designing engineers to analyse all the different type of financial and operational data of our company and create a summary of everything so that understanding of companies workflow can be simple. **What do you dislike about Databricks?** One of the major disadvantage of databricks is that the software is completely based on programming language like Python, SQL, Scala so if the user does not have knowledge of these programming languages than it is very difficult for them to use this platform. **What problems is Databricks solving and how is that benefiting you?** The best part of databricks is that it can easily get integrated with cloud networks like as azure, AWS, Google Cloud etc. So, working on this platform also help us in getting access to all the data of different years and compile them to get a proper analytics of business performance. **Official Response from Janelle Glover:** > It's great to hear that Databricks has helped you integrate with cloud networks and access historical data for business analytics. We understand that the programming language requirement may be a challenge for some users, and we appreciate you sharing your concerns with this. ### 45. Unified Data Workflows with Databricks **Rating:** 4.0/5.0 stars **Reviewed by:** Sayli G. | AI@ML intern, Small-Business (50 or fewer emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** I really like Databricks for its collaborative lake house environment, which has been key in unifying our data engineering and machine learning workflows. It bridges the gap between our engineering and analytics teams, allowing us to run BI and AI on a single platform. Additionally, the initial setup was surprisingly fast from a workspace perspective, especially with the native integration in Azure. **What do you dislike about Databricks?** The learning curve is quite steep for non-engineers. We've also had to be very diligent with cost monitoring as auto scaling clusters quickly lead to unexpected expenses if not managed strictly. **What problems is Databricks solving and how is that benefiting you?** Databricks solved our data stack fragmentation by unifying storage lakes and warehouses. It bridged the gap between engineering and analytics, letting us run BI and AI on a single platform. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the collaborative lake house environment and the unification of data engineering and machine learning workflows with Databricks. It's great to hear that the initial setup was fast and that the native integration in Azure has been beneficial for you. ### 46. Efficient Data Scaling with Collaborative Notebooks, but Costly **Rating:** 3.5/5.0 stars **Reviewed by:** Avinash J. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2026 **What do you like best about Databricks?** I use Databricks for ETL workflows and appreciate how it solves the problem of handling massive volumes of data using Apache Spark. Instead of dealing with complex cluster infrastructure manually, Databricks provides a managed environment that helps in scaling. One of the best features is the collaborative notebook environment, which allows cross teams to collaborate effectively. I switched from Snowflake to Databricks mainly because of the massive parallel processing of Spark. Although the initial setup was tough, learning Databricks is easy. **What do you dislike about Databricks?** Biggest issue is cost management. Initial setup was tough. **What problems is Databricks solving and how is that benefiting you?** Databricks handles massive data volumes with Apache Spark without manually managing complex cluster infrastructure, providing a scalable managed environment. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks efficient for handling massive volumes of data using Apache Spark and that the collaborative notebook environment is beneficial for cross-team collaboration. ### 47. Efficient Cluster Management, But Expensive **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really appreciate the ease of provisioning nodes with Databricks. Before, when using SQL Server, I had to contact the infra team to get clusters, and finding the right size was tough. Now, I can get and size clusters on my own, and that's a big advantage for me. Also, Databricks helped us migrate all our data analytics from SQL Server, making our process easier. The team from Databricks was supportive in helping us get started with the initial setup despite the learning curve. **What do you dislike about Databricks?** Still I feel like EIA has a long way to go. And, it is very expensive compared to all other partners. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for processing billions of records, making our analytics easier. We can provision and size clusters ourselves, improving flexibility compared to SQL Server. **Official Response from Janelle Glover:** > We're glad to hear that you appreciate the ease of provisioning nodes and our team's support. We understand your concerns about cost, and we're constantly working to improve our offerings to deliver the best value to our customers. ### 48. Great Governance and UI—Databricks Fits Our ETL Workflow Perfectly **Rating:** 4.0/5.0 stars **Reviewed by:** Vijay Krishna B. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** I like the overall environment, especially the governance features and the way the UI is handled. I primarily use Databricks as my ETL platform, and it fits well with how I work. The SDP job management governance and lineage capabilities are also helpful. **What do you dislike about Databricks?** Sometimes there are glitches in the UI. For example, if I cancel something, it takes a bit longer for that change to be reflected in the UI. **What problems is Databricks solving and how is that benefiting you?** It addresses centralized database and lakehouse management through Unity Catalog. It has also helped solve governance needs and improved lineage tracking. **Official Response from Janelle Glover:** > We're glad to hear that Databricks is fitting well with your ETL workflow and that you find the governance features and UI helpful. We appreciate your feedback about the glitches in the UI, and we'll pass this on to our team for further review. ### 49. Seamless Big Data Processing with Robust Access Control **Rating:** 4.0/5.0 stars **Reviewed by:** Adarsh C. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** April 14, 2026 **What do you like best about Databricks?** I use Databricks for big data processing and data engineering with PySpark. It helps me process terabytes of data seamlessly using Spark architecture. I love the Unity catalog and its access framework, which allows me to share data across the organization without much trouble and control access like Select, View, and others on delta tables based on roles or teams. The initial setup was seamless, and I appreciate how it integrates with Microsoft Fabric. **What do you dislike about Databricks?** I believe the billing experience can be improved; I use Databricks through Azure. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to process terabytes of data seamlessly using Spark architecture. The Unity Catalog helps me share data across the organization effortlessly, controlling access to delta tables based on roles or teams. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the seamless big data processing and robust access control features of Databricks, especially with PySpark. We appreciate your feedback on the billing experience and will take it into consideration for future improvements. ### 50. Unified Platform with Scalability and ML Power for Big Data **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** April 21, 2026 **What do you like best about Databricks?** I like Databricks for its unified platform, which brings data engineering, analytics, and machine learning together. It simplifies workflow scaling and is easy for handling big data. The collaboration across the team is much smoother, which I really appreciate. **What do you dislike about Databricks?** I would say cost transparency maybe. User-based pricing can be hard to predict. So the initial setup and cluster configuration can feel complex. Better documentation for that and UI could be more intuitive in some areas. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to sort ETL pipelines, handle large-scale data efficiently, reduce data processing time, and eliminate data silos. The unified platform improves collaboration between data engineers and scientists, simplifying workflows and making big data management smoother. **Official Response from Jess Darnell:** > Thank you for sharing your positive experience with Databricks' unified platform and its impact on collaboration and big data management. We acknowledge your feedback regarding cost transparency and cluster configuration complexity, and we're committed to enhancing our documentation and UI to provide a more intuitive experience. ## 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, 1 upvote - [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?filters%5Bnps_score%5D%5B%5D=4§ion=pricing&secure%5Bexpires_at%5D=2026-08-10+03%3A26%3A12+-0500&secure%5Bsession_id%5D=225817cb-3d48-42fe-94ee-b5546f6c84da&secure%5Btoken%5D=732807d229fbfc038de296199266987cb5b9ec7f196a8361b0ed720433137304&format=llm_user) ## Databricks Integrations - 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[Azure Logic Apps](https://www.g2.com/products/azure-logic-apps/reviews) - [Azure OpenAI Service](https://www.g2.com/products/azure-openai-service/reviews) - [Azure Pipelines](https://www.g2.com/products/azure-pipelines/reviews) - [Azure Portal](https://www.g2.com/products/azure-portal/reviews) - [Azure SQL Database](https://www.g2.com/products/azure-sql-database/reviews) - [Base SAS](https://www.g2.com/products/base-sas/reviews) - [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) - [Claude Code](https://www.g2.com/products/anthropic-claude-code/reviews) - [Crunchbase](https://www.g2.com/products/crunchbase/reviews) - [Dash](https://www.g2.com/products/dash-for-brands-ltd-dash/reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [DigitalOcean](https://www.g2.com/products/digitalocean/reviews) - [Domo](https://www.g2.com/products/domo/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - 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[Microsoft SharePoint](https://www.g2.com/products/microsoft-sharepoint/reviews) - [Microsoft SQL Server](https://www.g2.com/products/microsoft-sql-server/reviews) - [Microsoft Teams](https://www.g2.com/products/microsoft-teams/reviews) - [MLflow](https://www.g2.com/products/mlflow-mlflow/reviews) - [MySQL](https://www.g2.com/products/mysql/reviews) - [ObjectWay SpA](https://www.g2.com/products/objectway-spa/reviews) - [Pega Platform](https://www.g2.com/products/pega-platform/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [PowerBI Portal](https://www.g2.com/products/powerbi-portal/reviews) - [Prophecy](https://www.g2.com/products/prophecy-prophecy/reviews) - [Salesforce Agentforce](https://www.g2.com/products/salesforce-agentforce/reviews) - [Salesforce Headless 360 Platform (formerly Salesforce Platform)](https://www.g2.com/products/agentforce-360-platform-formerly-salesforce-platform/reviews) - [SAP Ariba](https://www.g2.com/products/sap-ariba/reviews) - [SAP ECC](https://www.g2.com/products/sap-ecc/reviews) - [Seamless (formally Seamless.AI)](https://www.g2.com/products/seamless-formally-seamless-ai/reviews) - [ServiceNow IT Service Management](https://www.g2.com/products/servicenow-it-service-management/reviews) - [Sisense](https://www.g2.com/products/sisense/reviews) - [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) - [Unity Catalog Command Center](https://www.g2.com/products/unity-catalog-command-center/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 - Customizable Reports - Marketing Reports - Sales Reports - Activity Dashboard - Interactive Reports - Customizable Reports - Customizable Reports - Activity Dashboard - Customizable Dashboard **Additional Functionality** - Continuous Integration - Drag & Drop - Backup and Recovery - Multiple Programming Languages Supported - Continuous Deployment - Code Development - For No-Code Development - Version Control - Configurable Workflow - Graphical User Interface - For Low-Code Development - Activity Dashboard - Generative AI - UI Prototyping - Source Control - Software Development - API - Data Import/Export - Web App Development - Custom Development - Code Repository Integration - Data Security - Mobile Development - Access Controls/Permissions - Game Development - Collaboration Tools - Application Security - Alerts/Notifications - Automated Testing - Integrated Development Environment - Offline Access - Debugging - AI Copilot - Reporting/Analytics - Compatibility Testing - Pre-built Templates - Third-Party Integrations - Data Modeling - Customizable Branding - Data Visualization - Code Editing **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Data Cleansing - Data Extraction - Third-Party Integrations - Data Verification - Information Governance - Data Capture and Transfer - AI Copilot - Customizable Reports - Data Migration - Behavior Analytics - Real-Time Reporting - Interactive queries - Audit Trail - Multiple Data Sources - Document Storage - Access Controls/Permissions - User Management - AI/Machine Learning - Master Data Management - Task Scheduling - Data Import/Export - Monitoring - Customer Database - Audit Management - Reporting/Analytics - Data Visualization - Collaboration Tools - Workflow Management - Full Text Search - Compliance Management - Generative AI - Data Quality Control - Data Connectors - Automatic Backup - Activity Tracking - Activity Dashboard - Data Security - Data Analysis Tools - Data Synchronization - Metadata Management - Dashboard Creation - Visual Analytics - Secure Data Storage - Data Integration - Database Support **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations - Configurable Workflow - Rules-Based Workflow **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling - Real-Time Data **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics - ETL - Extract Transfer Load - Data Capture and Transfer - Real-Time Analytics - Reporting/Analytics **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage - Data Visualization **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Reports** - Reports Interface - Share Reports - Steps to Answer **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Functionality** - Ease of Use - File Management - Multi-Language Support - Customization - Straight-Out-the-Box Functionality - Help Guides - Patching & Updates - Workflow Management - Change Management - Deployment Management - Performance Management - Compliance Management - Lifecycle Management - Task Management - Document Management - Database Support **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 - API Security - Data Security - Authentication **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 **Configuration** - Application Performance - Orchestration - Database Monitoring - Anomaly Detection - Network Security **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training - Database Support - Multi-Language **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying - Reporting/Analytics - Predictive Analytics - Visual Analytics **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring - Real-Time Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability - Non-Relational Transformations - Data Extraction **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending - Data Capture and Transfer **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Security** - Access Control - Roles Management - Compliance Management - Deletion Management - Access Controls/Permissions - User Management - Process Management - Audit Management - Metadata Management **Operations** - Metrics - Infrastructure management - Collaboration **Visualization** - Graphs and Charts - Score Cards - Dashboards - Formats - Mobile Dashboards - Public Dashboards - Private Dashboards **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 - Multimedia Support - Multi-Modal Input Support **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Additional Functionality** - Data Mapping - Monitoring - Charting - Integration Management - Reporting/Analytics - Ad hoc Analysis - Access Controls/Permissions - API - Match & Merge - Real-Time Monitoring - Metadata Management - Pipeline Management - Job Scheduling - Dashboard Creation - Data Storage Management - Multiple Data Sources - Data Import/Export - Generative AI - Data Quality Control - Data Connectors - Customizable Reports - Single Sign On - Version Control - Visual Analytics - Accounting Integration - Real-Time Data - eCommerce Management - AI Copilot - CRM - Data Visualization - SSL Security - Search/Filter - Real-Time Analytics - Data Capture and Transfer - Collaboration Tools - Performance Management - Data Synchronization - Drag & Drop - Data Replication - Activity Dashboard - Database Support - Workflow Management - Alerts/Notifications - Predictive Analytics - Data Migration - Third-Party Integrations - Reporting & Statistics - Data Analysis Tools **Database Administration** - Provisioning - Governance - Auditing **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 **Maintainence** - Data Quality Management - Policy Management - Data Storage Management **Management** - Cataloging - Monitoring - Governing **Data Updates** - Historical Snapshots - Real-Time Updating **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 **Availability** - Scalability - Backup - Archiving - Indexing **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake - Real-Time Data - Data Capture and Transfer - Trend Analysis - What-if Analysis - Statistical Analysis - Data Blending - Ad hoc Analysis - Third-Party Integrations **Security** - Data Masking - Authentication And Single Sign-On - Data Anonymization **Performance ** - Scalability **Collaboration** - Sharing - Co-Editing - Devices **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 - Third-Party Integrations **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Additional Functionality** - Customizable Reports - Collaboration Tools - Data Extraction - Semantic Search - Data Storage Management - Ad hoc Reporting - Reporting/Analytics - Predictive Analytics - Activity Dashboard - Access Controls/Permissions - Visual Analytics - Data Mapping - Data Synchronization - Statistical Analysis - Categorization/Grouping - Trend Analysis - Data Profiling - Linked Data Management - Data Visualization - API - Multiple Data Sources - Sentiment Analysis - Search/Filter - Data Import/Export - Data Capture and Transfer - AI Copilot - Monitoring - Data Connectors - Ad hoc Analysis - Text Mining - Reporting & Statistics - Predictive Modeling - Real-Time Analytics - Configurable Workflow - Tagging - Endpoint Management - No-Code - Data Preparation - Auditing - Big Data Analytics - ML Algorithm Library - Data Management - Activity Tracking - Data Security - Workflow Management **Additional Functionality** - Version Control - Scalability - Personalization - Data Extraction - Webhooks - API - Natural Language Processing - Fallback Handling - Drag & Drop - Multiple LLM Models - Built-in AI Assistant - Automated Testing - Data Governance - Collaboration Tools - Pre-built Templates - Agent Design Tools - Deep Learning - Model Training - Analytics - Single Sign On - Debugging - Deployment Management - Proactive Error Detection **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling - Natural Language Search - Visual Discovery - Data Blending - Data Blending **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks - Data Discovery **Data Management** - Data Replication - Advanced Data Analytics **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image - Generative AI **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 - Third-Party Integrations - Active Directory Integration **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity - Multiple Data Sources - Query Builder **Additional Functionality** - AI Copilot - Reporting & Statistics - Data Migration - Archiving & Retention - Data Capture and Transfer - Audit Trail - Data Synchronization - Single Sign On - Customizable Reports - Data Profiling - Authentication - Role-Based Permissions - Tagging - Secure Data Storage - Data Security - HIPAA Compliant - SSL Security - Risk Assessment - Data Mapping - Search/Filter - Self Service Portal - Multiple Data Sources - Document Storage - API - Activity Tracking - Automatic Backup - Visual Analytics **Additional Functionality** - Data Warehousing - Drag & Drop - Activity Dashboard - Data Connectors - Ad hoc Reporting - Customizable Reports - Alerts/Escalation - Templates - Forecasting - Data Migration - Data Transformation - Access Controls/Permissions - API - Data Cleansing - Data Synchronization - AI Copilot - Dashboard Creation - Data Extraction - Data Security - SSL Security - Collaboration Tools - No-Code - High Volume Processing - Database Support - Search/Filter - Generative AI **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services - Real-Time Analytics - Reporting/Analytics - Real-Time Analytics - Reporting/Analytics - Visual Analytics **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 - Third-Party Integrations **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support - Database Support **Additional Functionality** - Parallel Processing - Ad hoc Analysis - Multiple Data Sources - API - In-Database Processing - Monitoring - Real-Time Reporting - Data Synchronization - Real-Time Monitoring - Data Connectors - Performance Metrics - Ad hoc Reporting - Alerts/Notifications - Access Controls/Permissions - Drag & Drop - Data Visualization - Data Import/Export - Match & Merge - Data Transformation - Secure Data Storage - Data Extraction - AI Copilot - Customizable Reports - Activity Dashboard - Data Migration - In-Memory Processing - Data Mapping **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - Auto-generated Insights and Narratives - Natural Language Queries - Proactive KPI Monitoring and Alerts - AI Agents for Analytical Follow-ups **Personalized Intelligence - Analytics Platforms** - Behavioral Learning for Contextual Query Refinement - Role-based Insight Personalization - Conversational and Prompt-based Analytics - Ad hoc Query - Visual Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management **Data Updates** - Historical Snapshots - Real-Time Updating - Scheduled/Automated Reports - Customizable Reports - Predictive Analytics - Data Management - Real-Time Data **Additional Functionality** - Drag & Drop - Secure Data Storage - Data Import/Export - Customizable Branding - Dashboard Creation - Access Controls/Permissions - Real-Time Reporting - Metadata Management - Forecasting - Data Storage Management - Ad hoc Analysis - Ad hoc Reporting - Data Migration - AI Copilot - Self Service Data Preparation - Search/Filter - Data Mapping - Monitoring - Sentiment Analysis - User Management - Sales Trend Analysis - Charting - Reporting & Statistics - Performance Metrics - Alerts/Notifications - Data Extraction - Widgets - Storytelling - Trend Analysis - Collaboration Tools - Financing Management - Self-service Analytics - KPI Monitoring - Dashboard - API - Data Integration - Scorecards - Data Management - Task Management - Progress Tracking - Data Connectors - Data Visualization - Profitability Analysis - Project Tracking - Strategic Planning - Goal Setting/Tracking - Publishing/Sharing - Predictive Analytics - Real-Time Monitoring - Templates - Mobile Access - Customizable Templates - Workflow Management - Financial Reporting - Audit Management - OLAP - Single Sign On - Data Synchronization **Additional Functionality** - Customizable Branding - Natural Language Processing - Data Extraction - AI Copilot - Ad hoc Reporting - Real-Time Reporting - Publishing/Sharing - Collaboration Tools - Strategic Planning - Self Service Data Preparation - Trend Analysis - Text Analysis - Real-Time Monitoring - Data Synchronization - Widgets - Benchmarking - Performance Metrics - Data Mapping - Generative AI - Trend/Problem Indicators - Customizable Templates - Access Controls/Permissions - Data Import/Export - Profitability Analysis - OLAP - Alerts/Notifications - Search/Filter - Drag & Drop - Data Connectors - Multiple Data Sources - Key Performance Indicators - Dashboard Creation - Role-Based Permissions - Data Mining - Forecasting - Ad hoc Query **Additional Functionality** - KPI Monitoring - Secure Data Storage - Multiple Data Sources - Data Visualization - Single Sign On - Natural Language Search - Search/Filter - Alerts/Notifications - AI Copilot - Real-Time Notifications - Collaboration Tools - Widgets - Performance Metrics - Ad hoc Reporting - Activity Tracking - Reporting/Analytics - Customizable Branding - Data Aggregation - Dashboard Creation - Data Connectors - Customizable Templates - Data Synchronization - API - Forecasting - Interactive Elements - Single Page View - Data Import/Export - Drag & Drop - Access Controls/Permissions - Trend Analysis - Workflow Management - Third-Party Integrations - Functions/Calculations - Historical Reporting - Data Capture and Transfer - Visual Discovery - Real-Time Updates - Real-Time Reporting - Data Mapping - Relational Display - Visual Analytics - OLAP - Ad hoc Query - Reporting & Statistics ## Top Databricks Alternatives - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 4.1/5.0 (131 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (714 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (356 reviews)