--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1335 reviews by the users' company size, role or industry to find out how Databricks works for a business like yours. aggregate_rating: rating_value: 4.6 review_count: 1335 scale: '5' date_modified: '2026-06-23' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Databricks Reviews **Vendor:** Databricks Inc. **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.6/5.0 **Total Reviews:** 1,335 ## About Databricks Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. ## Databricks Pros & Cons **What users like:** - Users appreciate the **AI features and robust data security** of Databricks, enhancing their data management capabilities significantly. (288 reviews) - Users appreciate the **ease of use** of Databricks, enhancing their workflow with efficient model hosting and management. (278 reviews) - Users value the **seamless integrations** of Databricks, enhancing efficiency and collaboration across data workflows. (189 reviews) - Users value the **excellent collaborative environment** in Databricks, enhancing teamwork for data engineers and analysts. (150 reviews) - Users value the **effective data management features** of Databricks, enhancing usability and decision-making across enterprises. (150 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (148 reviews) - Users value the **integrated analytical features** of Databricks, enhancing operations and providing comprehensive insights on technology. (139 reviews) - Machine Learning (136 reviews) - ML Integration (135 reviews) - Scalability (134 reviews) **What users dislike:** - Users face a **steep learning curve** with Databricks, complicating adoption and resource management for newcomers. (112 reviews) - Users find the **cost high** for working on large data, which can impact budget management. (97 reviews) - Users face a **steep learning curve** with Databricks, making initial adoption and resource management challenging. (96 reviews) - Users find the **missing features** in Databricks limiting, affecting usability and development timelines. (69 reviews) - Users find the **complexity** in learning structured streaming and integration issues challenging, impacting their overall experience with Databricks. (64 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (61 reviews) - Performance Issues (57 reviews) - Poor UI Design (53 reviews) - Difficult Learning (51 reviews) - Users experience a **complex setup** process initially, but support helps to simplify the experience over time. (45 reviews) ## Databricks Reviews ### 1. Great Spark Scaling, But Slow Cluster Boot Times **Rating:** 5.0/5.0 stars **Reviewed by:** Raj P. | Marketing Coordinate, Mid-Market (51-1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** I start my day by opening up Databricks notebooks to clean and process raw data logs. The collaborative workspace is easily one of my favorite parts. When my team and I are working on the same notebook, the real-time co-authoring makes debugging incredibly easy. The Managed Apache Spark engine is a lifesaver for training heavy machine learning models because the clusters scale up automatically without me needing to worry about infrastructure. I just write my Python or SQL code, and it handles the heavy lifting in the background. Once my ad-hoc analysis is done, it is seamless to connect the data directly to Power BI so the business teams can view live dashboards. **What do you dislike about Databricks?** The biggest pain point is definitely when I have an urgent report due or a quick request from a manager first thing in the morning, and the cluster takes 5 to 10 minutes to spin up. Sitting there staring at the "Starting Cluster" status when you are on a tight deadline is deeply frustrating. Also, the pricing and DBU (Databricks Unit) model can be confusing to wrap your head around, and costs quickly spiral if you forget to set up auto-termination properly. I also occasionally run into version conflicts when installing custom Python libraries on specific runtimes, which can eat up half an hour just troubleshooting environment errors. **What problems is Databricks solving and how is that benefiting you?** Before using Databricks, our workflow was completely fragmented, with data engineering pipelines running in one place and our data models sitting on local machines, causing massive data redundancy. Switching to the Lakehouse architecture completely centralized everything and streamlined our daily routines. It has removed the traditional wall between data engineers and data analyst also data science, allowing us to collaborate seamlessly on a single platform. My honest assessment is that while it is expensive and the boot times are annoying, it is an incredibly robust platform that is hard to beat for heavy data workloads and serious data science. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on your experience with Databricks. We are thrilled to hear that you are enjoying the collaborative workspace and the Managed Apache Spark engine for scaling up clusters effortlessly. We understand your frustration with slow cluster boot times and the challenges with pricing clarity and version conflicts. Your insights are invaluable to us, and we are continuously working to enhance our platform. We appreciate your patience and understanding as we strive to improve the user experience. If you encounter any issues or have suggestions for improvement, please feel free to reach out to our support team. We are here to assist you and ensure that your data processing tasks are as efficient as possible. Thank you for choosing Databricks for your data workloads and data science needs. ### 2. Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines **Rating:** 4.5/5.0 stars **Reviewed by:** Konjengbam M. | BDR, Financial Services, Mid-Market (51-1000 emp.) **Reviewed Date:** June 10, 2026 **What do you like best about Databricks?** I love this platform for its capability to handle big pool of data efficiently. I love the idea of Data Lakehouse of this platform. The Collaborative work supported by this platform greatly enhances productivity and team work. In our context of using the Databricks SQL query we can easily identify the best match of entrepreneurs needed to avail a specific scheme. This saves both time and increases efficiency. The capacity of this platform to analyze the past performance and trends enable us to map out a highly targeted approach that is really efficient. I love the capability of this platform as it can identify probable stress asset which might be an issue for future investment. This makes us rethink our strategy and approach towards the best and efficient way forward. I feel that the platform have a user friendly interface. I love the integration ability of this platform as it integrates with most of the major platform. This makes this platform more robust and powerful. The onboarding of this platform is also easy as we could easily login with our Google ID. Other than this I love the ability of this platform to create pipeline. The capability of this platform to create agents also ease up tasks. It also enhances capability to handle work load more effectively and efficiently. **What do you dislike about Databricks?** I love most part of this platform but I have to admit that user will need a some training for the user to be more efficient. I feel that having a more technical experience will adapt well with this platform. I also wish that the pricing of this platform was also more moderate. Frankly saying the accuracy of output of this platform depends on how clean the Data is . So there is always a chance of spilling in the unclean data. This will directly impact the result. **What problems is Databricks solving and how is that benefiting you?** Frankly this platform translates into assistance in accurate decision making. The decision made by using this platform directly impacts productivity and averts risk. **Official Response from Jess Darnell:** > We're thrilled to hear that you find Databricks powerful and efficient for handling big data, collaboration, and creating pipelines. We appreciate your feedback on the user-friendly interface, integration capabilities, and easy onboarding process. ### 3. I love databricks **Rating:** 5.0/5.0 stars **Reviewed by:** taka b. | engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 11, 2025 **What do you like best about Databricks?** What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments. **What do you dislike about Databricks?** One downside is that the initial setup and networking configuration can be complex and require technical expertise. Also, the cost can scale up quickly depending on usage, so cost monitoring is essential. Additionally, the lack of comprehensive documentation in some languages like Japanese can be a limitation. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve challenges related to managing and analyzing large volumes of data across multiple sources. It simplifies ETL pipelines, improves data reliability through Delta Lake, and enables scalable machine learning. This has significantly reduced the time our team spends on data preparation and model training, leading to faster business insights and better decision-making. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks has helped solve challenges related to managing and analyzing large volumes of data for your team, leading to faster business insights and better decision-making. We appreciate your support and are committed to continuously improving our platform. ### 4. Databricks Simplified Our Large-Scale Data Workflows and Team Collaboration **Rating:** 5.0/5.0 stars **Reviewed by:** Kareena M. | Social Media Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** June 04, 2026 **What do you like best about Databricks?** What I personally liked most was how easy it became to handle large-scale data workflows in one place. Earlier we were using separate tools for processing, notebooks, and collaboration which became messy very fast. With Databricks, the team could collaborate on notebooks, run pipelines, and experiment with ML models without constantly switching environments. **What do you dislike about Databricks?** The pricing can become expensive if clusters are not managed properly, especially for smaller teams or startups. There’s definitely a learning curve as well if someone is coming from traditional SQL-only environments. **What problems is Databricks solving and how is that benefiting you?** We mainly used Databricks for centralized data engineering and analytics workflows. It helped us reduce dependency on multiple tools and improved collaboration between analysts and engineering teams. One major improvement was faster ETL processing and better visibility into data pipelines. It also made it easier to experiment with AI/ML use cases because the infrastructure setup was already integrated into the platform. **Official Response from Aunalisa Arellano:** > It's fantastic to hear how Databricks has benefited your team by centralizing data engineering and analytics workflows, reducing dependency on multiple tools, and improving collaboration. We're pleased to hear about the improvements in ETL processing and AI/ML experimentation. ### 5. 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. ### 6. Lakebase - Good Option for Low Latency Data Serving with Databricks Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Associate Director, Mid-Market (51-1000 emp.) **Reviewed Date:** March 25, 2026 **What do you like best about Databricks?** I have setup the lakebase which is in sync with lakehouse for enterpise application low latency access, we have enabled the below options - Lakebase using Postgres 17,HTTPS Data API is helping to access database in easier way from Databricks Apps - Integration sync between Lakehouse and Lakebase for API access - It has branching option to maintain schema or feature changes - Restore from previous point/time history - helps reducing recovery effort - Snapshots and backup support - Autoscaling compute and suspend option - Monitoring, logs and query metrics are giving visibility on active queries, performance and database health - OAuth access and postgres role based connection is helping better security and controlled access Lakebase pricing model with autoscaling and scale down option based on available compute pricing **What do you dislike about Databricks?** Lakebase all postgress features not available, so we can't directly migrate any existing postgres directly to lakebase Scale down to zero not happens instantly faced some issues like disconnect from app for short time temporary pauses Custom admin operations in database postgres are limited and not posisble **What problems is Databricks solving and how is that benefiting you?** Lakebase helps serve near real-time inventory data,availability tracking and reducing out-of-stock situation Processed Lakehouse data can be served through Lakebase for customer buying pattern, product recommendation and promotion effectiveness Transactional sales data can be made available quickly for store level dashboards, helping business teams monitor sales trend usings web apps Near real-time operational data availability applications access low latency data for pricing, promotions for TPO-TPM integrations Also we integrated the AI to include the real time context Low latency **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Genie, including its ability to bridge the gap between business and data teams, eliminate data silos, and improve cost and performance visibility. We understand your concerns about the limitations of Agent Mode and the need for further autonomy. We will work on addressing these areas to enhance your overall experience. ### 7. All-in-One Delta Lake Platform That Makes ETL Fast and Cost-Efficient **Rating:** 5.0/5.0 stars **Reviewed by:** Kavipriya S. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** May 27, 2026 **What do you like best about Databricks?** Delta Lake + Workflows + Unity Catalog in one platform eliminated the need for stitching together separate ingestion, transformation, and governance tools. As a data engineer, I spend more time building pipelines and less time managing infrastructure. The notebook experience and cluster auto-scaling make iterating on complex ETL fast and cost-efficient. **What do you dislike about Databricks?** Cluster spin-up times and cost predictability are still the biggest friction points for me. Cold starts can really slow down ad-hoc work, and DBU costs need close monitoring to avoid unpleasant surprises. The Workflows UI has improved a lot over time, but it still doesn’t feel as flexible as dedicated orchestrators when you’re dealing with more complex DAGs. Even so, I see these as mostly polish items—the platform’s core value easily outweighs them. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses a major fragmentation problem in our data engineering stack. Previously, we relied on separate tools for ingestion, transformation, orchestration, and governance—each with its own learning curve, maintenance overhead, and potential failure points. Now, it’s consolidated into a single platform. In practice, it helps us run large-scale ETL pipelines that process millions of records daily, with Delta Lake improving reliability through ACID transactions, schema enforcement, and time travel for debugging. It also closes the collaboration gap between data engineers and data scientists: we build the pipelines, and they can consume the same tables directly in notebooks without data duplication or sync issues. Unity Catalog resolved a long-standing governance headache by centralizing access control across workspaces. Overall, the result is faster pipeline development, fewer production incidents tied to data quality problems, and far less glue code to maintain. What used to take weeks to build and stabilize now takes days. **Official Response from Jess Darnell:** > We're delighted to hear that Databricks has consolidated your data engineering stack and improved the reliability of your ETL pipelines. We understand your feedback about cluster spin-up times and cost predictability, and we're actively working to optimize these aspects of our platform to provide a better user experience. ### 8. 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. ### 9. Making data systems less messy with a unified Lakehouse approach **Rating:** 5.0/5.0 stars **Reviewed by:** Hunar M. | Data Analyst, Geospatial Intelligence - Data & Analytics, Enterprise (> 1000 emp.) **Reviewed Date:** May 21, 2026 **What do you like best about Databricks?** The ecosystem. What I like most about Databricks is how it removes a lot of the usual mess you run into with data work. Instead of juggling separate tools for engineering, analytics, and ML—and then spending extra time getting them to talk to each other—it brings everything into one place. That alone cuts down a lot of friction and saves time. I also like the Lakehouse idea because it feels genuinely practical: you don’t have to choose between a data lake and a warehouse. You can work with one unified setup and still get performance when you need it. On a day-to-day level, it’s also nice that different teams can collaborate in the same environment without constantly copying data around or rebuilding pipelines. Overall, it keeps things simpler and faster, especially when you’re iterating. **What do you dislike about Databricks?** What I don’t like about Databricks is that it can feel a bit heavy when you’re just trying to do something simple. There’s a lot going on under the hood, and while that’s great for scaling, it also comes with a learning curve. Things like clusters, configurations, and job setup take some time to get comfortable with. Cost is another concern. Usage can creep up quickly if you’re not actively monitoring it, especially when teams can spin up compute freely. And at times, the overall experience feels a little fragmented across notebooks, jobs, and repos, rather than being one smooth, unified flow. So, yes—it’s powerful, but it definitely takes discipline to keep things clean, efficient, and under control. **What problems is Databricks solving and how is that benefiting you?** What Databricks really solves for me is the usual friction that shows up when data systems are spread across too many tools. Instead of running one system for ingestion, another for storage, something else for transformation, and then separate setups again for analytics and ML, it brings most of that into one place. That means I don’t have to keep moving data around or constantly worry about things drifting out of sync. From a solution architecture perspective, that’s a big win because it simplifies the overall design. Rather than stitching together a bunch of systems, you can build around a single Lakehouse setup that supports multiple use cases. It’s easier to scale, easier to govern, and overall just easier to reason about. On a day-to-day basis, it also means I spend less time on infrastructure and plumbing and more time thinking through how to design good data models and pipelines. And because everyone is working from the same data, there’s much less confusion and rework between teams. Overall, it removes a lot of the noise and lets me focus on building solid, scalable data solutions. **Official Response from Jess Darnell:** > We're glad to hear that you find our ecosystem and Lakehouse approach beneficial for simplifying and unifying your data work. We understand your concerns about the learning curve and cost, and we're continuously working to improve the user experience and provide cost-effective solutions. Thank you for sharing your thorough feedback with us. ### 10. Databricks centralizes data, analytics, and AI **Rating:** 5.0/5.0 stars **Reviewed by:** Leonardo Q. | RPA Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 16, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it centralizes data engineering, analytics, and AI in a single platform, which greatly facilitates the workflow on a daily basis. The integration between notebooks, pipelines, and distributed processing makes development faster and more organized, especially in projects with a large volume of data and automations. Another point that I consider very strong is the experience with Apache Spark, integrated in a simplified way. Even in more complex scenarios, the performance is usually excellent, allowing large-scale data processing with good stability and scalability. This greatly helps in integrations, ETLs, and analyses that, in other solutions, would require much more effort. **What do you dislike about Databricks?** Although I quite like the platform, some aspects of Databricks can still be challenging. The main one is the cost, especially in environments with intensive processing or when clusters are not well optimized. Without more rigorous usage control, expenses can increase rapidly. Another aspect is the learning curve, which can be steep for teams that are starting in the distributed data ecosystem. Concepts related to Spark, clusters, optimization, and resource management require time to adapt, especially for those coming from more traditional tools. In UI/UX, although the interface is generally good, some administrative processes and more advanced configurations can seem confusing at first. In certain scenarios, identifying performance or permission issues may also require more technical knowledge. **What problems is Databricks solving and how is that benefiting you?** Databricks has primarily helped to solve problems related to the centralization, processing, and analysis of large volumes of data. Previously, many processes were distributed among different tools, which made integrations, maintenance, and governance difficult. With Databricks, a large part of the data engineering, analytics, and AI workflow can be concentrated on a single platform, bringing more consistency to daily work. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks centralization of data, analytics, and AI to be beneficial for your workflow. We understand the importance of integration and simplification, and we're committed to providing a platform that meets your needs. ### 11. Databricks Simplifies Big Data Processing and Team Collaboration **Rating:** 4.5/5.0 stars **Reviewed by:** Praveen M. | Associate Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** May 07, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it simplifies large-scale data processing and collaboration in one platform. The integration with Spark and cloud service makes handling big data much more efficient. I also like the notebook environment, which makes it easier for teams for works together on analytics and machine learning tasks. **What do you dislike about Databricks?** One thing I dislike about Databricks is the platform can feel complex for new users, especially when managing clusters and configurations. Pricing can also become expensive with larger workloads if resources are not optimized carefully. While integrations and AI features are powerful, the onboarding process and support documentation could be more beginner-friendly. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of processing and analyzing large amounts of data efficiently in one platform. It combines data engineering, analytics and AI workflows, which reduce the need for the multiple separate tools. This improves collaboration, speeds up data processing, and helps generate insights much faster. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks helpful for simplifying large-scale data processing and collaboration. Our integration with Spark and cloud services is designed to make handling big data more efficient. ### 12. Perfect for Cross-team Collaboration and Intensive Data Applications **Rating:** 5.0/5.0 stars **Reviewed by:** Artemij V. | Data Science Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** May 04, 2026 **What do you like best about Databricks?** The UX is one of the strongest parts. The notebook experience is clean and intuitive, collaboration is straightforward, and moving between exploration, experimentation, and production workflows feels seamless. It has enough flexibility for advanced users while still being approachable enough that onboarding new team members is fast. People can usually become productive quickly without spending weeks learning platform-specific quirks. The integrations are also excellent. It works smoothly with the broader cloud ecosystem and connects well with data sources, orchestration tools, model serving infrastructure, and external systems. That interoperability makes it much easier to move from prototype to deployed pipeline without constantly rebuilding connectors or managing glue code. Performance has been consistently strong, especially when working with distributed workloads and large-scale feature engineering. Spark optimization, cluster management, and managed infrastructure significantly reduce operational overhead, which lets me focus more on model development and analysis rather than environment tuning. For iterative experimentation, spin-up times and overall responsiveness are noticeably better than many alternative managed platforms. **What do you dislike about Databricks?** One area where Databricks could improve is pricing. The platform delivers strong capabilities, but costs can escalate quickly for high-frequency or real-time workloads. For use cases involving continuously running low-latency tick pipelines, streaming market data, or iterative model retraining, the pricing can become fairly steep relative to the infrastructure being consumed. It sometimes feels like there’s a meaningful premium for convenience and managed orchestration, which can make cost optimization a constant consideration. The AI integration is another area that still feels somewhat uneven. While there’s a clear push toward positioning the platform as an end-to-end AI/ML environment, some of the newer AI-focused features feel more like ecosystem additions than deeply integrated workflow improvements. In practice, there are still cases where custom tooling or external frameworks provide more flexibility and transparency, particularly for specialized model development, experimentation, and real-time inference use cases. There can also be some complexity around tuning clusters and managing costs efficiently at scale. While the abstractions are helpful, getting the best performance-to-cost ratio sometimes requires deeper platform knowledge than the “fully managed” positioning might imply. Overall, the platform is very strong technically, but pricing for always-on data-intensive workloads and the maturity of some AI-native capabilities are the two biggest areas where I’d like to see improvement. **What problems is Databricks solving and how is that benefiting you?** Databricks solves one of the biggest challenges in modern data work: bringing together data access, large-scale processing, and collaborative development in a single environment. For my work, the biggest benefit is real-time collaboration. It allows multiple people to work against the same datasets, notebooks, and pipelines without the usual friction of fragmented tooling or environment inconsistencies. That significantly speeds up experimentation, iteration, and knowledge sharing across projects, especially when moving quickly on model development or analyzing fast-changing data. It also solves the challenge of scalable data access and processing. Working with high-volume time-series and transactional datasets requires infrastructure that can process large amounts of data efficiently without constant operational overhead. Databricks abstracts much of that complexity, making it possible to focus on analysis, feature engineering, and model development rather than spending time managing infrastructure. The practical benefit is faster iteration cycles. I can move from raw data exploration to model experimentation and deployment much more quickly, which is especially valuable when working on real-time analytics, forecasting pipelines, and production-facing ML systems where speed of iteration directly impacts outcomes. Overall, it reduces engineering friction and makes large-scale collaborative data work significantly more efficient, which translates into faster development, better experimentation, and more reliable deployment of data products. **Official Response from Jess Darnell:** > We appreciate your thorough review of Databricks and are pleased to hear that the platform has been instrumental in enabling cross-team collaboration and intensive data applications for your work. Your feedback on pricing and AI integration is valuable, and we are continuously striving to enhance these aspects to provide a more seamless experience for our users. ### 13. Genie Code and Inline Assistant Dramatically Boosted My Debugging Productivity **Rating:** 4.5/5.0 stars **Reviewed by:** Shyam s. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Genie code and the inline Assistant were the most helpful tools for me on my project. They helped me debug a 2k-line codebase and clearly explained why I wasn’t getting accurate data. It also provided a query to run in my source system (SQLMI). By running the discrepancy script in parallel on the source and target, I was able to debug the entire code much faster and improve my productivity. Overall, it cut my work time from about 8 hours down to around 1 hour. **What do you dislike about Databricks?** In Delta Sharing, there isn’t a catalog-level SELECT permission, and I sometimes think having that would be helpful. Also, when I use the Genie code inside a VM, it can make the website unresponsive at times. These are areas that could be improved. **What problems is Databricks solving and how is that benefiting you?** In one of our claims-processing migration projects, the client needed near real-time data availability for downstream applications. Previously, the architecture used Amazon Redshift as the data warehouse, with Jasper and Sisense consuming the data for reporting and analytics. However, that setup didn’t support real-time or near real-time streaming efficiently, which led to delays in data availability for downstream systems. After migrating the platform to Databricks, we were able to substantially improve the data pipeline architecture. We implemented streaming along with optimized ETL pipelines, reducing the data refresh cycle to about 30 minutes. We also created a dedicated view that retains data from the previous run, so downstream systems always have a consistent dataset available while the next pipeline execution is still in progress. Before, we struggled with delayed refresh cycles and a limited ability to meet near real-time data needs in our Redshift-based architecture. After moving to Databricks, we enabled faster ETL processing and improved near real-time data availability. As a result, we reduced ETL refresh time to roughly 30 minutes and enabled near real-time access for downstream tools like Jasper and Sisense. Reliability also improved because the stable view continues to serve the previous run’s data during pipeline updates. Finally, the overall architecture became simpler by consolidating processing and analytics capabilities within Databricks. Overall, Databricks helped us build a more scalable and efficient near real-time data processing platform, significantly improving the timeliness and reliability of analytics for the claims-processing workflow. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks' architecture is benefiting you. We designed our platform to address the challenges of managing structured and unstructured data, and it's great to hear that it's making a positive impact on your analytics and machine learning workflows. ### 14. Databricks Makes Large-Scale Data Transformations Easy to Run **Rating:** 5.0/5.0 stars **Reviewed by:** Gunther C. | Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 05, 2026 **What do you like best about Databricks?** Databricks simplifies the process of running data transformation operations on massive datasets. Although it can be a bit of a paradigm shift from classic asynchronous processing architectures, it is extremely easy to get started with. Simply put, the thing I like best about it is it's ability to do work at scale. **What do you dislike about Databricks?** The inability to run a copy of Databricks locally to test changes before deploying them to production is a significant hindrance. Creating per-developer staging environments might be a close solution l, but might be a lot of work to manage. **What problems is Databricks solving and how is that benefiting you?** Databricks is making it possible to process tremendous amounts of data efficiently, while simultaneously not requiring a large amount of engineering effort to be applied towards designing the system itself (engineers can focus on solving data problems rather than scaling problems) **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear that you find it easy to run data transformations at scale. We understand your concern about not being able to run a local copy for testing. We are continuously working on improving our platform and will take your feedback into consideration. Thanks for taking the time to leave a review. ### 15. The Unified Data Platform That Actually Delivers **Rating:** 5.0/5.0 stars **Reviewed by:** Janakiraman K. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks has transformed how our team handles end-to-end data workflows. A few standouts: UI/UX: The notebook interface is intuitive, and the SQL editor feels polished which switching between Python, SQL, and Scala in the same workspace saves constant context-switching. Integrations: Native connectors to Azure, Unity Catalog, and Delta Sharing mean we spend less time on plumbing. Lakehouse Federation lets us query external sources without moving data, which was an unexpected win. Performance: Delta Lake's auto-optimization and liquid clustering noticeably reduced our query times. Photon engine on heavy aggregations is a game-changer for near real-time dashboards. Pricing/ROI: The DBU model takes getting used to, but consolidating our data warehouse, ETL, and ML tooling into one platform cut our overall infrastructure spend significantly. Support/Onboarding: Databricks Academy and the built-in documentation made onboarding new engineers faster. The community forum is surprisingly active for niche questions. AI/Intelligence: Genie (AI/BI) lets business users ask questions in plain English and get accurate results reducing ad hoc requests to our data team by a noticeable margin. Databricks Assistant inside notebooks also accelerates code generation and debugging. **What do you dislike about Databricks?** While Databricks is powerful, there are real friction points worth mentioning: UI/UX: The interface can feel overwhelming for new users have the navigation between Workspaces, Catalogs, and SQL Warehouses isn't always intuitive. Folder and notebook organization could be more structured out of the box. Integrations: Some third-party connectors still require manual configuration and custom code. Lakehouse Federation is promising but occasionally inconsistent with certain source systems, needing extra troubleshooting. Performance: Cluster startup times remain a pain point cold starts on interactive clusters can disrupt fast-paced workflows. Serverless compute helps but isn't universally available across all features yet. Pricing/ROI: The DBU-based pricing model lacks transparency for newer teams. It's easy to rack up unexpected costs without careful cluster policies and monitoring in place. A more straightforward cost estimator would help significantly. Support/Onboarding: Enterprise support response times can be slow for non-critical tickets. For complex architectural issues, getting to the right expert often takes multiple escalations. AI/Intelligence: Genie works well for standard queries but struggles with complex multi-table logic or domain-specific terminology without significant fine-tuning. The Databricks Assistant inside notebooks occasionally generates outdated or incorrect API suggestions. **What problems is Databricks solving and how is that benefiting you?** Here's a natural, story-driven answer following the "before/after/result" format: Before Databricks, our data landscape was fragmented and separate tools for ETL, warehousing, and ML meant duplicated pipelines, inconsistent data definitions, and significant engineering overhead just to maintain the plumbing. Data Unification: We struggled with siloed data across multiple source systems. Now, with Unity Catalog and the Medallion architecture (Bronze/Silver/Gold), we have a single governed layer that all teams trust reducing data reconciliation effort by nearly 40%. Pipeline Reliability: Building and maintaining metadata-driven pipelines used to require custom frameworks. Databricks' Lakeflow and Delta Live Tables gave us incremental and full-load capabilities out of the box, cutting pipeline development time significantly. Self-Service Analytics: Business teams constantly depended on engineers for ad hoc queries. With Genie (AI/BI), stakeholders can now ask plain-English questions against curated gold tables reducing ad hoc data requests to our team noticeably week over week. Cloud Cost Control: We previously ran always-on clusters without visibility into spend. Serverless compute and cluster policies now let us right-size workloads, resulting in measurable infrastructure cost reduction. Faster Onboarding: New engineers previously took weeks to get productive. With Databricks Assistant, notebook templates, and centralized Unity Catalog documentation, ramp-up time has dropped considerably. Overall: Databricks essentially replaced 3-4 separate tools with one cohesive platform the ROI isn't just in cost savings, it's in the speed and confidence with which we now deliver data products to the business. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks valuable for data engineering, analytics, and machine learning. Thanks for sharing your feedback! ### 16. Scalable, All-in-One Environment with Some Learning Curve **Rating:** 5.0/5.0 stars **Reviewed by:** Antonio V. | Data & AI Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2026 **What do you like best about Databricks?** I like Databricks for its scalability and all-in-one environment for data engineering, analytics, and machine learning. It allows me to process large datasets efficiently while keeping workflows organized in one platform. The scalability is very valuable because it lets me handle growing data volumes and complex workloads without performance issues. As projects expand, the platform can scale resources efficiently. **What do you dislike about Databricks?** Some features can have a learning curve, especially for new users working with advanced configurations or cluster management. The interface could also be more intuitive in certain areas. The setup was relatively smooth for core features, but some advanced settings like cluster optimization, permissions, and integrations required more time and technical knowledge. **What problems is Databricks solving and how is that benefiting you?** Databricks solves major data management and analytics challenges by efficiently handling large datasets, simplifying ETL processes, and centralizing workflows. Its scalability allows me to manage growing data volumes without performance issues, ensuring resources scale efficiently as projects expand. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks scalable and appreciate its all-in-one environment for data engineering, analytics, and machine learning. We understand that some features may have a learning curve, and we are continuously working to improve the platform's usability and intuitiveness. ### 17. Databricks: Unified, Efficient at Scale with Seamless Cloud Integration **Rating:** 5.0/5.0 stars **Reviewed by:** ibrahim d. | Associate Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** Databricks provides a unified platform and is very efficient working with large scale terabytes level data. I also like the integration with various cloud services which is seamless and very helpful. Also, the inbuilt Apache spark and very efficient AI/ML workflow orchestration stands out from others. And the databricks support has been outstanding in case of any issues. **What do you dislike about Databricks?** With features comes cost and using databricks at a scale we use it (terrabytes data, multi customer, multi environment) becomes cost challenging. Also, learning curve can be bit steep for new beginners. **What problems is Databricks solving and how is that benefiting you?** Our primary challenge was managing large volume data for multiple customers and across different regions. Databricks very efficiently resolved that challenge with it unified platform and very good cloud integration. Our data pipelines are much faster and more orchestrated than ever. **Official Response from Jess Darnell:** > It's great to hear that Databricks has efficiently resolved your challenges with managing large volume data for multiple customers and regions, making your data pipelines faster and more orchestrated. We are happy to hear the inbuilt Apache spark and efficient AI/ML workflow orchestration stands out from others! ### 18. Performance with Spark and collaborative notebooks that make the data flow more efficient **Rating:** 5.0/5.0 stars **Reviewed by:** Homero F. | Professor particular, Mid-Market (51-1000 emp.) **Reviewed Date:** April 19, 2026 **What do you like best about Databricks?** What I like most is the performance in processing large volumes of data with Spark, the collaborative notebooks that facilitate teamwork, and the integrations with AWS and BI tools, which make the entire data flow more efficient. **What do you dislike about Databricks?** The cost can be high depending on the usage, and some parts of the interface, such as cluster and job configuration, are not very intuitive at first. Additionally, the learning curve can be somewhat steep for new users. **What problems is Databricks solving and how is that benefiting you?** Databricks solves problems of processing large volumes of data, integrating different sources, and developing AI models in a single environment. This improves our workflow, reduces processing time, and centralizes everything on the platform. Integrations with AWS and other tools facilitate implementation, and the support along with the documentation help with adaptation. Additionally, the AI resources allow for creating, training, and testing models more quickly and efficiently. **Official Response from Jess Darnell:** > Thank you for your positive feedback! ### 19. Powerful Unified Analytics with Seamless Governance and Effortless Scaling **Rating:** 4.5/5.0 stars **Reviewed by:** Akhil S. | Senior Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is its powerful and unified analytics ecosystem. Features like Unity Catalog and Metastore make data governance and access control seamless, while the Lakehouse architecture combines the best of data lakes and warehouses. PySpark support, dbutils, and collaborative workspaces make development efficient, and serverless compute simplifies scaling without infrastructure overhead. **What do you dislike about Databricks?** What I dislike about Databricks is the slow startup time of all-purpose clusters, which can interrupt workflow and reduce productivity. Additionally, Git integration can feel a bit sluggish at times, especially during commits or syncing, making version control less seamless than expected. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of managing end-to-end data workflows by providing a unified platform for data engineering, data science, and analytics. It allows seamless data processing, transformation, and model development within a single environment. This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable. **Official Response from Jess Darnell:** > We're pleased to hear that Databricks is simplifying your data workflows and providing seamless integration with Azure Data Factory. We take note of your concerns about slow startup times and Git integration, and we are committed to optimizing these aspects to ensure a smoother experience for our users. Your input helps us prioritize enhancements that align with our users' needs. ### 20. 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! ### 21. Databricks as a Hands On Data Engineer: Solving Real World ETL, Governance, and Lakehouse Challenges **Rating:** 5.0/5.0 stars **Reviewed by:** KAVIN P. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 08, 2026 **What do you like best about Databricks?** I believe the most attractive thing about Databricks lies in its all-in-one nature, which makes data management easier. Previously, when I used several tools for data-related activities, the experience was not great but here everything seems to be interconnected and straightforward. The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view. In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required. There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently. **What do you dislike about Databricks?** One aspect of Databricks that i dislike is its UI. As you spend longer in using the tool, moving between notebooks and clusters becomes annoying at times. The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects. There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines. At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be. **What problems is Databricks solving and how is that benefiting you?** The most important issue that Databricks resolves is the issue of working with large volumes of data and maintaining consistency. Previously, there were separate processes for data engineering, analytics, and machine learning operations, requiring separate tools and made it difficult for me to handle but now these all are in one place, another one critical issue solved by Databricks is the issue of processing large data volumes. Utilizing the Spark, and distributed computing allows it to perform the tasks that were extremely slow on legacy systems I worked with. This has helped speed up my pipeline, although some time the delays occur.Collaboration is also another problem that Databricks addresses. Multiple users can collaborate on the same notebook or data sets. Collaboration previously was confusing, and now it is easy and good and easy and easly understandable and mainly easy sharing notebooks and assets.Scalability is another issue resolved by Databricks; there is no need to pay attention to infrastructure management. Cluster scaling depends on user requirements, saving time. Previously, it was necessary to pay more attention to the configuration of the infrastructure. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' all-in-one nature and interconnectedness beneficial for data management to help your team save time. We appreciate your feedback on the advantages of utilizing notebooks and the efficiency in handling big data. ### 22. 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. ### 23. Reliable data platform with powerful pipeline support **Rating:** 4.5/5.0 stars **Reviewed by:** Chandhuru B. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 06, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning together in one clean workspace. It saves time, makes collaboration easier, and helps teams move faster with large data. **What do you dislike about Databricks?** What I dislike about Databricks is that Auto Loader can become frustrating when source data changes frequently, especially if column names or datatypes shift without warning. For example, a field like customer_id may suddenly come in as cust_id, or a column that was previously a string may start arriving as an integer, which can cause schema drift and break downstream processing. I also find it inconvenient when schema inference is not fully accurate, such as when nested JSON or semi-structured data is read incorrectly, because it then requires extra manual fixes and maintenance to keep pipelines running smoothly. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of building and managing data pipelines at scale without so much manual effort. It helps with reliable ingestion, schema evolution, and orchestration, so teams can process data faster and keep pipelines more stable even when source files change. For me, that means less time spent fixing broken jobs and more time focusing on transforming and using the data. It also benefits me by making batch and streaming workflows easier to manage in one platform, which is especially useful when data keeps changing. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a reliable platform for data engineering, analytics, and machine learning. We understand the frustration with Auto Loader when dealing with frequently changing source data. We are continuously working to improve schema inference accuracy and handling of nested JSON or semi-structured data to minimize manual fixes and maintenance for our users. ### 24. Love the Databricks and its Features and Unity Catalog for Streamlined Governance **Rating:** 4.5/5.0 stars **Reviewed by:** Prashant N. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** May 26, 2026 **What do you like best about Databricks?** In Databricks, I really like the newer features such as Gennie, the Databricks Assistant, agents, and the event-trigger mechanism. Also, the Unity Catalog feature is amazing. Having one place for all sources makes things much easier, and UC helps with governing tables in a more organized way. **What do you dislike about Databricks?** Nothing special to dislike, but there’s a feature to jump to a particular command. The feature itself is fine, but it’s placed right next to the notebook, which makes it easy to click accidentally, and that breaks my workflow. **What problems is Databricks solving and how is that benefiting you?** I am using it in my project for data processing and data quality analysis. With Databricks and its functionality, I am building agents in Genie space. Using UC, I am managing all managed and external tables in one place. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the newer features like Gennie, Databricks Assistant, agents, and the event-trigger mechanism, as well as the Unity Catalog feature. We appreciate your feedback! ### 25. Databricks: Unified Platform for Data Processing and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Banu Prakash M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like that Databricks brings everything into one place, making it unnecessary to use different tools for data processing, analytics, and pipeline work. It handles large data well, and we don't have to worry about managing clusters manually. Additionally, Databricks handles collaboration and experimentation well, making it easy to try out new things. **What do you dislike about Databricks?** In my point of view, the one area that can be improved is cost management. If clusters aren't monitored carefully, costs can increase faster than expected. One improvement that would help is better visibility into costs at a more detailed level. More built-in alerts or recommendations when costs start increasing unexpectedly would also be helpful. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us handle large datasets and build data pipelines. It simplifies data processing, transforming, and analysis using Spark and SQL, all in one place. It solves the problem of slow data processing spread across systems, managing infrastructure automatically and facilitating collaboration and experimentation. **Official Response from Janelle Glover:** > We're thrilled to hear that Databricks has been beneficial for handling large datasets and simplifying data processing and analysis for you. We appreciate your feedback on cost management and will explore ways to enhance cost visibility and provide better monitoring tools. ### 26. One-Stop Solution with Robust Security, Needs Better Handling of Large Datasets **Rating:** 0.0/5.0 stars **Reviewed by:** srikanth s. **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** I really appreciate the Databricks Unity Catalog for enforcing data policies, as it’s ready to use and offers enterprise-grade security that helps our data lakehouse with all access controls and compliance. It's a one hub solution, especially valuable for organizations that are tightly governed, like us. We also use Spark as a service, Phoenix, and Fortran Gemini quite a lot, which makes it a perfect solution for our data analytics and data hub. The initial setup was pretty straightforward, involving some architectural discussions with the Databricks team, and it went smoothly over a few months. **What do you dislike about Databricks?** When acquiring larger data sheets, we often see problems. We are still looking to better refine our data access patterns for end users, especially on bigger datasets which are heavy compute intensive. This makes interactive queries and what-if analysis take quite a bit of time, making them not so interactive for the end users. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for its enterprise-grade security and compliance, especially with the Unity Catalog, which aids in enforcing data policies and access control. It's a one-stop solution for data analytics and management, crucial for regulated industries like ours. **Official Response from Janelle Glover:** > We're glad to hear that you appreciate the enterprise-grade security and compliance features of Databricks, including Unity Catalog. We understand the importance of data policies and access control, especially in regulated industries, and we're committed to providing robust solutions in these areas. ### 27. Databricks is a taking over the world **Rating:** 5.0/5.0 stars **Reviewed by:** Matimba M. | SQL DBA, Mid-Market (51-1000 emp.) **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** Databricks has fundamentally changed how organizations approach Data Engineering, Machine Learning, and AI. What began as a strong analytics platform has grown into a unified ecosystem that lets teams build, govern, and scale data-driven solutions within a single environment. From a Data Engineering standpoint, Databricks streamlines the creation of modern data lakes and lakehouse architectures. Capabilities such as Delta Lake, Unity Catalog, and automated pipelines reduce operational complexity while strengthening data quality, governance, and overall reliability. For Machine Learning teams, Databricks offers an end-to-end workspace where data scientists, engineers, and business stakeholders can collaborate smoothly. With experiment tracking, model management, feature engineering, and scalable training, it shortens the path from early ideas to production-ready outcomes. Most impressive is Databricks’ pace of innovation in AI. The platform has positioned itself at the center of the Generative AI wave by bringing large language models, vector search, AI agents, and enterprise-grade governance directly into the Lakehouse architecture. This helps organizations move beyond experimentation and deploy AI solutions securely and at scale. The consistent, unified experience across Data Engineering, Analytics, Machine Learning, and AI makes Databricks feel like a strategic platform rather than just another tool. It helps break down silos, speed up innovation, and turn data assets into real business value. Databricks isn’t simply participating in the future of data and AI—it’s helping shape it. For organizations aiming to modernize their data platform and build enterprise AI capabilities, Databricks stands out as one of the most compelling options on the market today. **What do you dislike about Databricks?** Nothing stands out for me at the moment, aside from the diversity in how the world’s population is represented. **What problems is Databricks solving and how is that benefiting you?** Data Engineering Problems 1. Data Silos Organizations often have data scattered across databases, files, data warehouses, and cloud storage, which makes it hard to get a consistent view. Databricks solution: * Unified Lakehouse architecture * A single source of truth for both structured and unstructured data * Helps eliminate duplicate copies of data 2. Slow ETL Pipelines Traditional ETL processes can take hours to run and often require significant ongoing maintenance. Databricks solution: * Apache Spark-based distributed processing * Auto-scaling compute * Delta Live Tables for automated pipeline management 3. Poor Data Quality Bad data leads to unreliable reporting and weak AI outcomes. Databricks solution: * Delta Lake ACID transactions * Data quality expectations * Schema enforcement and evolution 4. High Infrastructure Costs Maintaining separate systems for storage, processing, and analytics can be expensive and inefficient. Databricks solution: * Separation of storage and compute * Serverless capabilities * Optimized resource utilization ⸻ Analytics Problems 5. Slow Reporting and Queries Large datasets can overwhelm traditional systems, resulting in slow reporting and long query times. Databricks solution: * Photon query engine * Distributed SQL processing * Near real-time analytics 6. Multiple Analytics Platforms When teams use different tools, it creates governance issues and makes collaboration harder. Databricks solution: * Unified SQL, Python, Scala, and R environment * Shared governance layer * Centralized data access ⸻ Machine Learning Problems 7. Models Never Reach Production Many ML projects get stuck in experimentation and never make it into production. Databricks solution: * End-to-end ML lifecycle management * MLflow integration * Model registry and deployment workflows 8. Difficult Collaboration Between Teams Data scientists, engineers, and analysts often work in isolation, which slows delivery and creates handoff friction. Databricks solution: * Collaborative notebooks * Shared workspaces * Unified data platform 9. Feature Duplication Teams often rebuild the same features repeatedly, wasting time and creating inconsistencies. Databricks solution: * Feature Store * Reusable feature management * Consistent training and inference data ⸻ AI & Generative AI Problems 10. Enterprise AI Cannot Access Business Data Securely LLMs are powerful, but they are often disconnected from company knowledge or cannot access it securely. Databricks solution: * Retrieval-Augmented Generation (RAG) * Vector Search * Unity Catalog governance 11. AI Hallucinations Generative AI can produce incorrect or misleading answers. Databricks solution: * Grounding models on enterprise data * RAG architectures * Model evaluation frameworks 12. AI Governance and Compliance Organizations need visibility into who accessed which data and which models were used. Databricks solution: * Unity Catalog * Lineage tracking * Centralized permissions and auditing **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear how our platform has revolutionized your approach to data engineering, machine learning, and AI. It's great to know that Databricks is effectively addressing your data engineering, analytics, machine learning, and AI challenges, helping you break down silos and drive real business value. If you have any specific suggestions or further feedback, please feel free to reach out. We're here to support you every step of the way and ensure you continue to benefit from our innovative solutions. Thank you for choosing Databricks! ### 28. A Reliable Workhorse for Data Engineering and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Supriya M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** The unified platform approach is what I appreciate most. Having notebooks, data engineering pipelines, ML workflows, and SQL analytics all in one place saves a ton of time instead of juggling multiple tools. The collaborative notebooks make it easy to share work with teammates, and the cluster management has gotten a lot smoother over time. Delta Lake integration is also a huge plus for keeping our data reliable and consistent. **What do you dislike about Databricks?** The cost can get out of hand pretty quickly if you're not careful with cluster sizing and uptime. It's not always obvious how to optimize spending, and the pricing model feels complex. The learning curve for new team members is also steeper than I'd like, especially for people who aren't already familiar with Spark. Sometimes the UI can feel sluggish when working with larger notebooks, and debugging job failures could be more straightforward. **What problems is Databricks solving and how is that benefiting you?** Databricks helps me resolve complex ETL pipeline failures and persistent data quality issues in supply chain analytics by unifying batch and streaming processing from SAP systems with Delta Live Tables. It also removes a lot of the infrastructure management headaches thanks to auto-scaling clusters, so I can stay focused on writing code for multi-terabyte workloads instead of constantly worrying about cluster sizing. For my manufacturing data projects, Databricks accelerates development cycles from weeks to days via collaborative notebooks and DLT pipelines, enabling faster Power BI reporting and stakeholder decisions. Unity Catalog centralizes governance across Azure and SAP sources, preventing schema drift that plagued prior Hive-based lakes. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of the unified platform approach and the time-saving features of Databricks. We understand your concerns about cost management and the learning curve, and we're continuously working to simplify our pricing model and improve the onboarding experience for new team members. It's great to hear how Databricks is helping you resolve complex ETL pipeline failures and accelerating development cycles for your manufacturing data projects. ### 29. All-in-One Powerhouse with Room for Pricing Clarity **Rating:** 4.5/5.0 stars **Reviewed by:** Thoufeeq A. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like that Databricks is an all-in-one powerhouse where I can do multiple works in one place. It's powerful to manage data from multiple sources and have it in a single UC to manage permissions with row-level security. I also appreciate that I can create experiments, run multiple models, and select the best one from logs, which was difficult on other platforms. Once I learned the setup, it's been easy and comfy to work with. **What do you dislike about Databricks?** I find it difficult to use the calculator to determine CPU serving endpoint prices because the documentation doesn't explicitly explain this. It only mentions 1 concurrency equals 1 DBU on the Azure page, which isn't clear. The pricing calculator has a single option for serving endpoints, labeled as medium with four DBU, but lacks separate options for GPU or CPU and their concurrency, making it hard to understand how it works properly. Initially, I also felt it was very tough to learn Databricks and manage deployments of workspaces, although it became easier over time. **What problems is Databricks solving and how is that benefiting you?** Databricks consolidates multiple tools into one platform, making it powerful and convenient. I can manage permissions with row-level security and easily run experiments to select the best models, all in one place. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We understand your concerns about the pricing calculator and will take your feedback into consideration to improve the clarity of our documentation. ### 30. 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. ### 31. Unified Data Engineering, Science, and Analytics in One Collaborative Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Sivabalan A. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** What I appreciate most about Databricks is its ability to unify data engineering, data science, and analytics on a single platform. The collaborative environment—especially the notebooks and integrated workflows—makes it much easier for teams with different skill levels to work together without constant context-switching. Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful. **What do you dislike about Databricks?** Cost management is one area that could be improved. While Databricks offers autoscaling and flexible cluster options, it’s easy for resource usage to escalate unexpectedly, especially with large datasets and long-running jobs. Keeping costs predictable often requires careful oversight and a solid understanding of the platform’s pricing model. Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses several key challenges in modern data workflows, particularly around scalability, data reliability, and collaborative analytics. One major problem it solves is managing and processing large-scale datasets efficiently. By leveraging Apache Spark’s distributed computing framework, Databricks enables parallelized ETL pipelines and large-scale data transformations that would be impractical on traditional infrastructure. Another challenge is ensuring data consistency and reliability across pipelines. With Delta Lake, Databricks provides ACID-compliant storage, versioned tables, and schema enforcement, which reduces data errors and simplifies data governance. This is especially beneficial when multiple teams are working on different stages of data pipelines at the same time. Databricks also helps solve the problem of fragmented workflows for data scientists and engineers. Its unified environment supports multiple languages (Python, SQL, R, Scala) and includes integrated machine learning with MLFlow, making it easier to collaborate and move from data preparation to analytics and ML in one place. **Official Response from Janelle Glover:** > It's great to hear how Databricks is helping address scalability, data reliability, and collaborative analytics challenges for your team. We appreciate your feedback on cost management and advanced feature usability. We are continuously working to improve our pricing transparency and enhance the user experience for all our features. ### 32. Databricks Unifies the Data & AI Lifecycle for Fast, Collaborative ML Workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Phillipe S. | BI & Analytics Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** What stands out most is the unified experience Databricks provides across the entire data and AI lifecycle. The ability to handle data engineering, machine learning, and AI workloads within a single platform — without constantly switching tools — is a significant productivity gain. The ML pipeline capabilities in particular are notably fast and intuitive, allowing our team to iterate quickly. The seamless integration with cloud infrastructure and the collaborative notebook environment have also made onboarding and cross-functional work much smoother. **What do you dislike about Databricks?** As a recent adopter, the main challenge has been the learning curve associated with platform administration and cost management. Understanding and optimizing cluster configurations, compute costs, and Unity Catalog governance requires a level of expertise that takes time to develop. Documentation is extensive but can sometimes be overwhelming for teams that are just getting started. A more guided onboarding experience for new enterprise customers would be a welcome improvement **What problems is Databricks solving and how is that benefiting you?** Databricks is helping us consolidate a previously fragmented data and AI ecosystem into a single, governed platform. Before the migration, managing separate tools for data engineering, analytics, and machine learning created significant operational overhead and slowed down our ability to deliver insights. With Databricks, we now have a unified environment that supports everything from data ingestion and transformation to model development and deployment. This consolidation is already accelerating our machine learning pipeline delivery and enabling our team to focus more on business value rather than infrastructure management. As we continue to mature our usage, we expect further gains in productivity, governance, and AI capability across the organization. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the unified experience Databricks provides across the entire data and AI lifecycle. Our goal is to make data engineering, machine learning, and AI workloads seamless and productive within a single platform. ### 33. Databricks as a True Enterprise Lakehouse—Unity Catalog and Lakeflow Shine **Rating:** 4.5/5.0 stars **Reviewed by:** Maxim O. | Senior Manager, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks works well when it is treated as an enterprise platform, not just a notebook environment. For a large wealth manager, it has enabled a firm-wide lakehouse spanning analytics, regulatory reporting, automated high-net-worth collateral generation, data reconciliation, and enterprise data mesh. The strongest feature is Unity Catalog. It makes data mesh operational by giving domain teams a governed way to publish, discover, secure, and consume trusted data products. Lakeflow Spark Declarative Pipelines and Lakeflow Connect have also been valuable for ingesting data from hundreds of source systems into governed Delta tables, with built-in quality expectations reducing custom validation work. For operational workloads, Photon, Job Clusters, Instance Pools, Databricks Workflows, and Databricks Asset Bundles have been especially useful. They allow teams to run repeatable, right-sized pipelines without maintaining DIY Spark infrastructure. One reconciliation framework now processes hundreds of millions of rows daily at materially lower compute cost than a VM-based Spark approach. Business impact includes faster time to insight, fewer manual reporting touchpoints, faster regulatory response, reusable data quality patterns, and a practical federated ownership model. **What do you dislike about Databricks?** Cost management is a watchout. Photon, Job Clusters, and Instance Pools can lower costs dramatically, but only when workloads are well-designed. Poorly tuned jobs, idle clusters, oversized compute, or duplicated pipelines can still burn budget fast. Finally, the platform creates some ecosystem dependency: once reconciliation, ingestion, orchestration, governance, and reporting patterns are Databricks-native, switching costs rise. In short, Databricks is a strong enterprise platform, but it rewards disciplined engineering and punishes casual adoption. **What problems is Databricks solving and how is that benefiting you?** Databricks is helping us solve the problem of fragmented, hard-to-govern enterprise data at scale. For a large wealth manager, it provides a unified lakehouse foundation for analytics, regulatory reporting, data reconciliation, collateral generation, and data mesh. Unity Catalog gives teams governed access to trusted data products, while Delta Lake, Lakeflow, Workflows, Photon, and Asset Bundles let us automate ingestion, quality checks, orchestration, and deployment patterns. The benefit is faster time to insight, fewer manual reporting touchpoints, more reliable regulatory response, reusable data quality controls, and materially lower compute cost versus traditional self-managed Spark infrastructure. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been valuable in enabling a firm-wide lakehouse and providing operational benefits such as faster time to insight and reusable data quality patterns. Thank you for sharing your positive experience with us. ### 34. Powerful unified platform for data, analytics, and AI **Rating:** 5.0/5.0 stars **Reviewed by:** Neha A. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks brings data engineering, analytics, machine learning, and AI workflows together in one platform, making collaboration much easier across teams. I especially value the flexibility of working with SQL, Python, and notebooks in the same environment, along with the scalability of Spark for large datasets. Features like Delta Lake, Unity Catalog, and the growing AI capabilities have helped streamline development, improve governance, and accelerate time to insight. **What do you dislike about Databricks?** While Databricks is a very powerful platform, the learning curve can be steep for new users, especially those without a distributed computing background. Some features and UI experiences can feel fragmented or evolve quickly, making it difficult to keep up with best practices. Cost management and cluster optimization also require careful monitoring to avoid unexpected expenses, particularly for teams new to the platform. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us process and analyze large volumes of data efficiently while bringing data engineering, analytics, and AI workflows into a single platform. It has reduced the need to move between multiple tools, improved collaboration across teams, and enabled faster development of reporting and data products. This has helped us deliver insights more quickly and scale our workloads with confidence. **Official Response from Jess Darnell:** > It's great to hear how Databricks has helped streamline development, improve governance, and accelerate time to insight for your team. We're committed to solving data processing and analysis challenges while providing a unified platform for data engineering, analytics, and AI workflows. ### 35. All-in-One Platform That Helps Us Iterate Fast and Deploy with Confidence **Rating:** 5.0/5.0 stars **Reviewed by:** Vijayaramuprawin V. | Sr. Cloud and DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** We use Databricks daily as our core data platform for building and running pipelines across a medallion architecture, from extracting data out of SAP and Arkieva all the way to reporting-ready datasets. The notebook experience is intuitive, the feature set is massive, and Asset Bundles have made our CI/CD story with Azure DevOps really solid. Integration with cloud services was smooth, and once things are set up they just work. The learning curve can be steep for newer team members, especially around things like Unity Catalog and DABs, and costs can creep up if you're not staying on top of cluster configurations. Support is decent and the docs are strong enough that we rarely need to open a ticket. Overall, it's a powerful platform that does a lot under one roof, and it's hard to imagine our data engineering workflow without it. **What do you dislike about Databricks?** The cost can creep up fast if you're not careful with cluster sizing and job configurations, so it takes some effort to keep things optimized. Also, the learning curve for newer team members can be steep, especially around things like Asset Bundles, Unity Catalog, and getting the CI/CD pieces wired up properly. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of having fragmented data spread across multiple systems like SAP and Arkieva by giving us one unified platform to extract, transform, and serve it all. That means our business teams get clean, reliable, reporting-ready data without us having to juggle a bunch of separate tools, and we can deploy and manage everything consistently across environments with confidence. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in streamlining your data engineering workflow and providing a powerful platform for your needs. We appreciate your feedback on the learning curve and cost considerations, and we're continuously working to improve in these areas. ### 36. Innovative AI Platform for Versatile Data Processing **Rating:** 4.5/5.0 stars **Reviewed by:** RAVI R. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Very innovative platform which provides ai capability to perform various data processing **What do you dislike about Databricks?** The Terraform provider is the biggest one. The account-vs-workspace provider split, the alias chains, and the fact that genuinely important operations aren’t first-class resources — so you end up dropping to terraform_data + local-exec curl against the Accounts API for things like PE approval or NCC bootstrap. That’s not declarative infra, that’s a shell script wearing an HCL costume. Add in the periodic provider bugs (the network policy resources have been rough) and state drift on resources that should be stable, and a lot of “Databricks on Terraform” is really “Databricks despite Terraform.” **What problems is Databricks solving and how is that benefiting you?** The core problem Databricks solves is the old data-lake-vs-warehouse split. You used to need a cheap, flexible lake for raw files and ML, plus a governed, fast warehouse for SQL and BI — and then spend your life shuffling data (and copies, and reconciliation bugs) between them. The lakehouse collapses that: Delta gives you ACID, schema enforcement, and warehouse-grade performance directly on cheap object storage. One copy of data, one governance model, serving both the analysts and the ML people. **Official Response from Jess Darnell:** > Thank you for sharing your feedback with us. We're glad to hear that you find Databricks to be an innovative platform for AI and data processing. We're sorry to hear about your experience with the Terraform provider. We appreciate your feedback and will share it with our team for further improvement. ### 37. Streamlined, Collaborative Data Workflows with Powerful Performance **Rating:** 5.0/5.0 stars **Reviewed by:** Dharun T. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it streamlines the entire data workflow by bringing processing, analysis, and machine learning into one platform. The collaborative notebook environment makes it easy to share code, context, and reasoning with teammates, which helps everyone stay aligned. It also performs strongly on large datasets while abstracting away most of the cluster management, so I can focus on solving the problem rather than dealing with infrastructure. On top of that, centralized access control and clear visibility into data usage support responsible data governance, offering a solid balance between power and ease of use. **What do you dislike about Databricks?** Databricks has a few downsides, although many of them feel more like trade-offs than outright negatives. My biggest concern is cost: if clusters aren’t managed carefully, expenses can climb quickly, even though the platform can scale very efficiently when it’s tuned properly. There’s also a real learning curve with Spark and distributed computing concepts, and debugging or performance tuning can be more involved than with simpler tools. Lastly, because it’s a managed service, you give up some low-level control compared with self-hosted systems, but the upside is that it takes a lot of the operational and infrastructure work off your plate. **What problems is Databricks solving and how is that benefiting you?** Because my client needs secure, reusable code, Databricks helps us write Python efficiently while applying OOP principles and design patterns. It also makes it straightforward to extend functionality over time and build custom code that interacts with APIs and databases. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a powerful and streamlined platform for collaborative data workflows. We understand the concerns about cost management and the learning curve associated with distributed computing concepts. We continuously work to improve our platform and provide resources to help users optimize their usage and overcome challenges. ### 38. Robust Platform, Intuitive Yet Complex **Rating:** 5.0/5.0 stars **Reviewed by:** Emilio C. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks to import all of our SQL server data and transform it to the medallion layers for bronze, silver, and gold, which I find very intuitive. It allows us to create our BI reports using Power BI and utilize the Unity Catalog for governance. I appreciate the different sections and layers it provides. The data lake pipelines are something I use every day, and I like that I'm able to deploy all of our pipelines using code. Our management saw Databricks as a very robust platform and ecosystem, allowing us to build and expand faster. **What do you dislike about Databricks?** I think something that could be improved is having a governance dashboard or panel where we can control specific access to features for our developers. I'm a workspace admin and sometimes find it hard to control what new features Databricks releases and how available those features should be for specific sets of our data engineers or business analysts. I don't think there's a method for an admin to control those settings right now. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to import SQL data and transform it into medallion layers for BI reports. I like the intuitive sections and layers it provides, making it easy to deploy pipelines using code. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks intuitive and robust for your data transformation and BI reporting needs. We appreciate your feedback about the need for a governance dashboard and will share this with our product team for consideration. ### 39. Scalable, Reliable Unified Analytics & AI Platform with Strong Cloud Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Mithil M. | Senior Business Intelligence Developer, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Scalability for big data workloads. Unified analytics and AI platform. Delta Lake's reliability and performance. Strong integration with cloud platforms like aws.amazon.com⁠�, azure.microsoft.com⁠�, and cloud.google.com⁠�. Collaborative notebooks and workflow automation. **What do you dislike about Databricks?** One challenge I see with Databricks is that the platform evolves very quickly. New features, AI capabilities, and product enhancements are released frequently, which can create a steep learning curve for both customers and employees. However, I actually view this as a positive challenge because it reflects Databricks' commitment to innovation. I enjoy learning new technologies, so working in an environment that is constantly pushing the boundaries of data and AI would be motivating for me. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving one of the biggest challenges organizations face today: managing and extracting value from massive amounts of data that are often scattered across different systems. Its Lakehouse platform unifies data engineering, analytics, machine learning, and AI, allowing teams to work from a single source of truth instead of maintaining separate tools and data silos. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks scalable and reliable for big data workloads. Our unified analytics and AI platform, as well as strong cloud integration, are designed to provide a seamless experience for our users. ### 40. 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. ### 41. Versatile Platform with Seamless Data Handling **Rating:** 4.5/5.0 stars **Reviewed by:** Jack R. | Demand Planning Analyst, Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like Databricks because it's versatile and offers all the possibilities with pretty low effort, especially for data scientists. I'm impressed by how quickly the auto scale and infrastructure can be set up to achieve great outcomes. It's amazing how it simplifies everything from collecting and storing tabular customer data to analyzing and training models, leading to real impacts on optimizing supply chains. The Genie integration makes it easy to create dashboards in seconds using natural language. I really appreciate the availability of compute resources, and I use Genie code, Databricks Notebooks, and dashboards frequently. Databricks has been reliable throughout my experience, and it helps bridge gaps in a small workforce effectively. **What do you dislike about Databricks?** I hope that they continue to offer connectivity with Claude Code. And, just continue to expand the tool kits that they offer in terms of cloud skills to empower people who are a bit more technical. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for collecting and storing customer data, analyzing it, and training models. It simplifies optimizing supply chains and standing up dashboards quickly with Genie integration and robust compute resources. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been a reliable and effective platform for your data needs, and that you're enjoying Genie. We appreciate your feedback and will continue to focus on enhancing our connectivity options and expanding our tool kits to empower our users. ### 42. Effortless Data Automation and Model Deployment **Rating:** 5.0/5.0 stars **Reviewed by:** Jack K. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I appreciate that Databricks is very organized and offers a convenient UC for data storage and script writing capabilities. The ease with which I can update tables in UC and enable permissions is a big plus. I find model storing and deploying super convenient and structured, making it easy to select any preexisting model or one I've created and share it with users in apps or other services. It's also great that I can do a lot within just one workspace. The initial setup seems easy, which adds to the overall experience. **What do you dislike about Databricks?** I think copilot features could be smoother. Currently, there is no claude code integration unless working through an IDE and enabling Databricks connect. If going through Databricks, genie doesn’t provide the best results. **What problems is Databricks solving and how is that benefiting you?** Databricks automates tedious jobs for my company, making model deployment and organization super convenient. The seamless UC for data storage and script writing simplifies permissions. It provides a structured workspace where I can share models easily. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks organized and convenient for data storage and script writing. We appreciate your feedback on the Copilot features and will take it into consideration for future improvements. ### 43. 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. ### 44. Solid platform for data pipelines **Rating:** 5.0/5.0 stars **Reviewed by:** Sudipto M. | SAP HANA developer, Data Services and Business Object, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Here’s a shorter, casual version: ⭐⭐⭐⭐⭐ Solid platform for data pipelines Databricks has made our data engineering work so much smoother. Delta Live Tables and Delta Lake together are a great combo — pipelines are reliable, easy to set up, and the auto-scaling just works without much babysitting. Collaboration is seamless too; the notebook environment makes it easy for the team to work together without friction. Pricing can creep up as you scale, so keep an eye on that. But overall, highly recommend it for any team doing serious data work **What do you dislike about Databricks?** Pricing can get expensive fast, especially as your cluster usage scales up — you really need to stay on top of cost monitoring. The cold start times on clusters can also be frustrating when you just want to test something quickly. And while the platform has a lot of features, the learning curve for newer team members is steeper than expected. Documentation is decent but sometimes lags behind new feature releases. **What problems is Databricks solving and how is that benefiting you?** We needed a reliable, scalable way to manage our data pipelines without the overhead of stitching together multiple tools. Dealing with data quality issues, inconsistent pipeline failures, and lack of collaboration across the team was slowing us down. Databricks gave us a unified platform to handle ingestion, transformation, and orchestration all in one place. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that Delta Live Tables and Delta Lake have been a great combo for reliable and easy-to-set-up pipelines. We appreciate your feedback on pricing and cold start times, and we're committed to addressing these concerns. We're delighted to have helped your team with managing data pipelines more efficiently. ### 45. Exciting Automation with Databricks, but Authentication Needs Improvement **Rating:** 5.0/5.0 stars **Reviewed by:** Irma G. R. | Dual Language Math and Science Teacher, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I am really excited for the ops side and not having to monitor pipelines all the time. I love having a tool that is able to do that for me and lets me know in an email format where there are failures and how they are prioritized. I'm very excited about all the products that were released today. **What do you dislike about Databricks?** I think it's the authentication for the multiple users once we actually build a use case and an application. If I want to give a user access to a Databricks app, they have to have access to the Unity Catalog, to the warehouse, to the tables, to the schema. And if they want to write back, they also have to navigate those certain aspects, and it's kind of hard, especially as we want to scale some of the use cases. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to enable applications or data into enterprises, helping me get out of my company's silo and see new opportunities. It automates pipeline monitoring, sending prioritized alerts via email when failures occur. **Official Response from Jess Darnell:** > It's great to hear that Databricks is helping you automate pipeline monitoring and enabling new opportunities. We understand your frustration with the authentication process and will work on making it more user-friendly for multiple users. ### 46. Fundamental for Our Data Strategy **Rating:** 5.0/5.0 stars **Reviewed by:** Beto C. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I value most about Databricks is that it brings together in a single platform capabilities that previously required multiple tools. The user experience is intuitive and facilitates collaboration between different technical profiles. It also allows for easy scaling as the team's needs grow. Additionally, I highlight the speed in developing and deploying data solutions, along with its governance and centralized management capabilities, which provide greater confidence and operational efficiency. **What do you dislike about Databricks?** Although the overall experience has been very positive, there are aspects that could be improved. In some cases, the administration and configuration of certain advanced features can be complex and require specialized knowledge. It would also be useful to have greater consistency between the documentation and the speed at which new platform capabilities evolve. Finally, simpler visibility on costs, consumption, and resource optimization would help teams make more informed decisions and maximize the value obtained from the platform. **What problems is Databricks solving and how is that benefiting you?** Databricks allows us to integrate multiple data sources and process large volumes of information. It facilitates the centralization of processes, improves the observability of pipelines, and enhances collaboration between teams. This helps us respond quickly to needs, with confidence and stability in the data. **Official Response from Jess Darnell:** > It's great to hear how Databricks is helping you integrate data sources, centralize processes, and improve collaboration. We're committed to providing solutions that enable quick and confident responses to your team's needs. ### 47. Databricks: All-in-One Solution for Data and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** FABIN P. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** What I like most about Databricks is that it brings everything into one place, making it easy to work on data, build models, and manage workflows. It helps teams collaborate easily in real time. It also works very fast with large data using Apache Spark, and features like automation and Delta Lake make handling big data much simpler. **What do you dislike about Databricks?** One thing I dislike about Databricks is that it can be expensive, especially for large workloads. Sometimes the interface and setup can feel complex for beginners. Also, managing clusters and configurations can take some effort if you’re not very familiar with it. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the problem of handling large amounts of data efficiently. It brings data engineering, analysis, and machine learning into one platform. This removes the need to use multiple tools. It helps in faster data processing using Apache Spark. It makes collaboration easier for teams. It simplifies building and managing data pipelines. It improves data reliability with features like Delta Lake. It reduces manual work through automation. It saves time and effort in daily tasks. Overall, it helps me work faster and more efficiently with data. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in streamlining your data engineering workflow and providing a powerful platform for your needs. We appreciate your feedback on the learning curve and cost considerations, and we're continuously working to improve in these areas. ### 48. 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. ### 49. From Hive Chaos to Unity Catalog - Worth Every DBU **Rating:** 5.0/5.0 stars **Reviewed by:** Balakumaran R. | Data Team Lead, Enterprise (> 1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** Unity Catalog has been the single biggest value-add for our enterprise migration. We moved from a Hive Metastore architecture to Unity Catalog and gained centralized governance, lineage tracking, and fine-grained access control across all our data assets without bolting on third-party tools. For a multi-domain organization (finance, manufacturing, supply chain, procurement), having one catalog that enforces consistent naming and permissions across bronze, silver, gold, and platinum layers saved us weeks of manual policy work. UI/UX: The notebook experience with inline Spark SQL and PySpark, combined with the workspace file browser, makes it straightforward for our team to develop and test transformations iteratively. The SQL editor for ad-hoc queries against Unity Catalog tables is clean and responsive. Integrations: Native Delta Lake support means we don't manage format conversions. The Azure Key Vault integration via secret scopes (dbutils.secrets.get) keeps credentials out of code. ADF integration for orchestration in our V1 environment was seamless, and Databricks Asset Bundles (DAB) for V2 deployment give us a clean CI/CD path with databricks.yml configs targeting dev/qa/prod without custom scripting. Performance: Switching to CTEs over temp views in our Gold notebooks reduced cluster memory pressure noticeably. The ability to right-size clusters per environment (1 worker for dev, 3 for production) with Standard_D4ds_v5 nodes keeps costs predictable while maintaining performance for our batch ETL workloads. Pricing/ROI: The pay-as-you-go compute model paired with single-user security mode clusters means we're not over-provisioning. Consolidating our ETL, governance, and BI serving layer into one platform eliminated licensing for separate catalog, orchestration, and data quality tools. AI/Intelligence (Genie): Genie Spaces have been an unexpected win. Our business analysts in finance and supply chain can ask natural language questions against curated Gold/Platinum tables without writing SQL. It reduced the number of ad-hoc report requests coming to the data team by giving domain users a self-service path that still respects Unity Catalog permissions. Support/Onboarding: The documentation is thorough, and the skills-based approach to learning (bundles, Unity Catalog, jobs, SQL) maps well to how our team actually works. Onboarding new engineers to the V2 architecture took about half the time compared to V1 because the platform conventions (medallion architecture, asset bundles, catalog naming) are well-documented and consistent. **What do you dislike about Databricks?** UI/UX: The notebook editor still feels behind dedicated IDEs. No native multi-file search, limited refactoring support, and the git integration UI is clunky for teams managing dozens of notebooks across workflow bundles. We ended up doing all real development in VS Code and treating the Databricks workspace as a deployment target, which adds friction. The workspace file browser also doesn't handle folder structures well when you have 50+ notebooks organized by domain there's no filtering, tagging, or favorites. Integrations: Databricks Asset Bundles (DAB) are a step forward, but the documentation has gaps for complex multi-bundle deployments. We run a shared Global_Utilities bundle that other workflow bundles depend on, and getting cross-bundle references to work reliably across dev/qa/prod targets required significant trial and error. The ADF-to-Databricks integration works, but debugging failed pipeline runs means jumping between the ADF monitoring UI and Databricks job runs with no unified view. A tighter handshake between orchestration and compute monitoring would save hours of troubleshooting. Performance: Cluster cold-start times remain a pain point for development workflows. Spinning up a single-node Standard_D4ds_v5 cluster takes 4-7 minutes, which breaks flow when you're iterating on notebook logic. Serverless compute helps but isn't available for all workload types yet, and the cost premium is hard to justify for dev/test environments. Pricing/ROI: The DBU pricing model is opaque for capacity planning. Estimating monthly costs for a project with 30+ scheduled jobs, interactive development clusters, and SQL warehouse queries requires building custom spreadsheets because the built-in cost management tools don't give you a clear forecast by workflow or domain. We've been surprised by cost spikes from jobs that ran longer than expected with no easy way to set per-job budget alerts. Support/Onboarding: Enterprise support response times are inconsistent. Critical issues with Unity Catalog permissions during our migration took 3-5 business days for initial triage, which stalled our deployment timeline. The community forums are helpful for common patterns, but for Unity Catalog edge cases (cross-catalog lineage, complex permission inheritance), the knowledge base is thin. AI/Intelligence: Genie is promising but still rough for production use. It struggles with joins across more than 3-4 tables, sometimes generates incorrect SQL against our Gold layer, and there's no easy way to curate or correct its responses to improve accuracy over time. Our business users got excited, tried it, hit wrong answers on moderately complex questions, and lost trust. A feedback loop where domain experts can flag and correct Genie's outputs would make it genuinely production-ready. **What problems is Databricks solving and how is that benefiting you?** Data Governance Fragmentation → Unified Catalog We struggled with a Hive Metastore environment where table ownership, access control, and lineage were managed through a patchwork of manual documentation and custom scripts. After implementing Unity Catalog, we now have centralized governance across 4 catalog layers (bronze, silver, gold, platinum) spanning 6 business domains. What used to take a full-time data steward to track manually is now enforced automatically through catalog-level permissions and lineage. This cut our access provisioning time from days to under an hour per request. Siloed ETL Logic → Standardized Medallion Architecture Before Databricks, our ETL pipelines were inconsistent — different teams wrote transformations differently, with no shared utilities or patterns. We built a standardized framework (Batch_Utilities.py) with reusable functions for schema validation, merge operations, data quality checks, and audit column management. Every notebook across all domains now follows the same 7-cell structure. This reduced new notebook development time from 2-3 days to roughly 4 hours, and onboarding a new developer to the pattern takes a single afternoon instead of a week. Costly Report Refresh Failures → Reliable Pipeline Orchestration We had recurring issues with Power BI reports pulling stale or incomplete data because upstream jobs failed silently. With Databricks Jobs and metadata-driven pipeline tracking (pipeline status, start/end timestamps logged per run), we now catch failures at the transformation layer before they propagate to reports. Report data freshness issues dropped by approximately 80%, and our finance team stopped scheduling "data verification" meetings that used to consume 3-4 hours per week. Multi-Environment Deployment Chaos → Asset Bundles Deploying notebooks across dev, QA, and production used to involve manual file copies and environment-specific config edits — error-prone and slow. Databricks Asset Bundles gave us declarative databricks.yml configs with variable substitution per target. A deployment that took 45 minutes of manual steps now runs in under 5 minutes via CLI. We deploy with confidence because the same bundle definition is validated before it hits production. Self-Service Analytics Gap → Genie + Platinum Layer Business analysts in supply chain and finance were fully dependent on the data team for any ad-hoc analysis. By building denormalized Platinum tables optimized for reporting and exposing them through Genie Spaces, we enabled self-service querying in natural language. Early adoption has reduced ad-hoc report requests to the data team by roughly 30%, freeing up engineering capacity for new feature development. Cost Visibility → Right-Sized Compute We were over-provisioning clusters because we had no clear view of actual utilization. By standardizing on Standard_D4ds_v5 nodes with environment-specific worker counts (1 for dev/QA, 3 for production) and single-user security mode, we reduced our monthly compute spend by approximately 25% compared to the shared cluster model we ran in V1. **Official Response from Janelle Glover:** > We appreciate your detailed feedback on your experience with Databricks. It's great to hear that Unity Catalog, UI/UX, integrations, performance, Genie, and support/onboarding have positively impacted your enterprise migration. We understand the areas of improvement you've mentioned and will take them into consideration for future enhancements. ### 50. Databricks Supercharges Our Data Processing with Ease **Rating:** 4.5/5.0 stars **Reviewed by:** Keith M. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I enjoy using Databricks for its ease of use and the speed of querying. The ease of programming is also a big plus, along with many other features. I'm impressed with how Databricks handles data pipelines with boilerplate logic, allowing us to manage our data efficiently. It's great that once we identify popular queries, we can isolate that data into datamarts to enhance performance. Additionally, I appreciate that Databricks is more impressive than our previous solutions, like Oracle and Yellow Brick, offering a cost-effective manner to handle similar data. I'm happy with the helpful support from the Databricks team, and partner Intrada, who guided us effectively. Overall, I'm pleased with Databricks and would recommend it pretty highly, scoring a nine or ten for recommendation. **What do you dislike about Databricks?** I think the semantics with the Genie AI setup and metrics views could be improved. We're hoping these semantics would get more mature. More cross-enterprise content and non-semantic related content would be very helpful. **What problems is Databricks solving and how is that benefiting you?** Databricks consolidates our data and enables AI in our products, helping expand our offerings. It simplifies data governance and product problems, and its ease of use speeds up querying and programming. We use it to create pipelines for efficient data management and isolate data into datamarts. **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear that you find our platform easy to use, efficient for querying, and beneficial for managing data pipelines. Your feedback on the Genie AI setup and metrics views is invaluable, and we'll consider it for future improvements. We're committed to enhancing our platform to meet your needs better. If you have any specific suggestions or further feedback, please feel free to reach out. We appreciate your recommendation and look forward to continuing to support your data processing needs. ## Databricks Discussions - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [View Databricks pricing details and edition comparison](https://www.g2.com/products/databricks/reviews?source=search§ion=pricing&secure%5Bexpires_at%5D=2026-06-23+07%3A27%3A14+-0500&secure%5Bsession_id%5D=ee300caf-9d6d-4af5-a619-29d5c6979a1d&secure%5Btoken%5D=c28bbe982b38abd5909ea5e4f95626a6463d944e1bbb0a34624213c273c51153&format=llm_user) ## Databricks Integrations - 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Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics **Management** - Data dictionary - Data Replication - Query Language - Data Modeling - Performance Analysis **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Customization - AI Agent Builders** - Natural Language Configuration - Tone Customization - Security Guardrails **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Traffic Management & Performance - AI Gateways** - Token-Aware Rate Limiting - Semantic Caching - Multi-Model Routing & Fallbacks **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Maintenance** - Data Migration - Backup and Recovery - Multi-User Environment **Security** - Access Control - Roles Management - Compliance Management **Operations** - Metrics - Infrastructure management - Collaboration **Analytics** - Analytics capabilities - Dasboard visualizations **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Functionality - AI Agent Builders** - Omni-channel Support - Agent Branding - Proactive Response Capabilities - Seamless Human Escalation **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Integrations** - Hadoop Integration - Spark Integration **Data Quality** - Data Preparation - Data Distribution - Data Unification **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Deployment** - On-Premise - Cloud **Security** - Data Encryption - User Access Control **Maintainence** - Data Quality Management - Policy Management **Management** - Cataloging - Monitoring - Governing **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Data and Analytics - AI Agent Builders** - Analytics & Reporting - Contextual Awareness - Data Privacy Compliance **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Performance ** - Scalability **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Integration - AI Agent Builders** - Workflow Automation - API Usage - Platform Interoperability - CRM Data Integration **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Generative AI** - AI Text Generation - AI Text Summarization **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Governance** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Decision Making **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Generative AI** - AI Text Generation - AI Text Summarization **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - 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 **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## 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) - 4.5/5.0 (707 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (355 reviews)