I really like the power of its engine, since it’s based on Apache Spark, especially when processing large volumes of telemetry, diagnostic logs, or performance metrics from embedded devices during stress tests. I also appreciate the support for collaborative notebooks in Python and Scala, which makes data exploration, building fast ETL pipelines, and running models much easier. The integration with Unity Catalog for governance is good as well; I like it. Review collected by and hosted on G2.com.

I don’t like the cost model because if I’m not careful with the cluster size or query optimization, the bill at the end of the month can be quite a surprise. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I appreciate having a dependable source for figures essential to documentation, like installation records, site performance data, and warranty data that I can query directly rather than relying on outdated spreadsheets. I really like the Genie feature because it allows me to ask questions in plain language and get usable answers without waiting for someone else to process a request. It works quickly and can handle large record sets efficiently, which still impresses me compared to previous methods. The dashboards offer a standing view of frequently cited data, which helps me quickly assess any changes rather than manually re-checking each number. The initial setup of Databricks was quite easy as we didn't encounter any major issues. Review collected by and hosted on G2.com.

Data does not always travel between platforms as cleanly as I would expect. Where a table comes across from another system, it is not consistently available to me on this side, so I am left reconciling definitions that ought to match already. It is the one area where I feel the groundwork was laid later than it should have been. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I love that the Databricks notebook environment makes it easy for our data scientists and engineers to work in the same space without constantly handling code back and forth between separate tools, which used to slow down every model iteration. The integration with our streaming infrastructure has been genuinely solid, allowing us to pull transactions in continuously without a lot of custom plumbing on our end. I also appreciate that pushing score results back out to our decision engine is just as smooth. The performance and load have held up well during peak shopping periods when transaction volume spikes hard, and the pipeline manages to keep pace without falling behind. Review collected by and hosted on G2.com.

What I don't really like is the pricing for running continuous streaming workloads, which adds up fast compared to the batch jobs we used to run since clusters need to stay active rather than spin up on a schedule. Justifying the ongoing cost took some real modeling on our end before finance was fully onboard. The AI assistant built into their platform helps with general coding tasks but doesn't offer anything specific to fraud modeling itself, so any real intelligence in our detection logic still comes entirely from our own data science teamwork, not the platform. Review collected by and hosted on G2.com.

I especially like the ability to generate visualizations within the same platform, which saves me a lot of time by not having to constantly export the data to make the information understandable. The real-time collaboration capabilities and processing speed are undoubtedly excellent, and the fact that it can be installed on a laptop and allows me to view the team's progress is superb. The initial setup was surprisingly efficient. Review collected by and hosted on G2.com.

I believe that, although I am comfortable using IT tools, the user interface for quick report generation could be improved, making it more intuitive for people who are not used to writing code. In other words, the module should be a little more flexible, allowing dragging and dropping of elements as easily as the visualization tools. Review collected by and hosted on G2.com.

The platform offers optimized Photon engines on top that perform telemetry processing at great pace for big scale payloads. With serverless SQL warehouses, our teams can run large, concurrent queries without having to manage the scheduling of resources. With the centralized object governance in unity catalog, clear column level data lineage is supported across multi regional workspace deployments. Review collected by and hosted on G2.com.

The web notebook environment doesn't provide native Git branching control and often has state loops which are broken by concurrent code pushes. For complex spatial data visualizations, rendering of such data within active workspace cells will constantly be a memory allocation problem on the browser. Enterprise users identity synchronization configuration menu don't work properly when handling nested group permission rule updates. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.