My usual use case for Databricks as an end-user mostly involves exporting data. This typically entails writing directly into a web interface to get the data out, so probably with Python.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
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Databricks Streamlined Our ETL Migration with Delta Lake and Unified Analytics
What do you like best about the product?
Databricks transformed my day-to-day workflow, taking me from constant SQL Server/ADF headaches to scalable, unified analytics. Migrating stored procedures into Spark SQL notebooks was surprisingly smooth, and using Delta Lake MERGE instead of complicated UPDATE logic saved me weeks of rewriting.
The most helpful features for me have been Delta Lake’s ACID transactions and schema evolution, which handle my sparse shipment loads really well. Unity Catalog has also been a big win because it eliminates the back-and-forth of RDS access tickets by enabling governed table sharing. On top of that, Genie turns natural-language requests into production-ready Spark SQL almost instantly.
On the upside, autoscaling clusters have cut costs by about 70% compared with ADF’s always-on pipelines. I also like being able to combine PySpark and SQL in a single notebook, which makes complex joins and subqueries much easier to manage. And I don’t miss the old NOLOCK hint debates—built-in optimizations take care of that.
If you’re migrating ETL pipelines, Databricks removes a lot of the SQL-to-cloud friction while still scaling to enterprise volumes without breaking the bank.
The most helpful features for me have been Delta Lake’s ACID transactions and schema evolution, which handle my sparse shipment loads really well. Unity Catalog has also been a big win because it eliminates the back-and-forth of RDS access tickets by enabling governed table sharing. On top of that, Genie turns natural-language requests into production-ready Spark SQL almost instantly.
On the upside, autoscaling clusters have cut costs by about 70% compared with ADF’s always-on pipelines. I also like being able to combine PySpark and SQL in a single notebook, which makes complex joins and subqueries much easier to manage. And I don’t miss the old NOLOCK hint debates—built-in optimizations take care of that.
If you’re migrating ETL pipelines, Databricks removes a lot of the SQL-to-cloud friction while still scaling to enterprise volumes without breaking the bank.
What do you dislike about the product?
The cluster reconnects fairly often, which can be disruptive during active work sessions. Also, when I run complex or heavy queries, I notice clear lag in response times, and that slowdown can hurt productivity.
What problems is the product solving and how is that benefiting you?
Databricks has helped us centralize our data engineering and analytics workflows into a single, unified platform. It addresses the challenge of managing large-scale data pipelines by enabling our team to process and transform massive datasets efficiently with Spark. The collaborative notebook environment has also boosted productivity, making it easier for data engineers and analysts to work together. Overall, it has significantly reduced the time we spend on data preparation and has allowed us to focus more on deriving insights.
Fast, Governed Self-Service Data Exploration with Databricks Genie
What do you like best about the product?
As a data engineer, I use Databricks Genie to interact with data in natural language, while still relying on the same governed tables, metrics, and semantic models that my team has built. Instead of jumping straight into SQL notebooks for every exploratory ask, I or business users can phrase questions in plain language and let Genie translate them into structured, catalog‑aware queries. This keeps self‑service fast but also secure and governed.
What do you dislike about the product?
Laptop stability when multitasking
My laptop can hang or become noticeably sluggish when I’m working with multiple Genie tabs and dashboards at the same time, especially during heavier queries or more demanding visualizations. This hurts the overall user experience and can slow down iterative development and analysis.
Latency with complex data models
With very wide schemas or more complex semantic models, Genie sometimes selects suboptimal joins or an overly broad/narrow level of granularity. As a result, I still need to review the generated SQL and optimize it myself. In that sense, it remains a helpful assistant rather than a fully autonomous query engine.
My laptop can hang or become noticeably sluggish when I’m working with multiple Genie tabs and dashboards at the same time, especially during heavier queries or more demanding visualizations. This hurts the overall user experience and can slow down iterative development and analysis.
Latency with complex data models
With very wide schemas or more complex semantic models, Genie sometimes selects suboptimal joins or an overly broad/narrow level of granularity. As a result, I still need to review the generated SQL and optimize it myself. In that sense, it remains a helpful assistant rather than a fully autonomous query engine.
What problems is the product solving and how is that benefiting you?
In a recent project, the business wanted to understand a decline in customer‑lifetime‑value (CLV) in a specific region. A product manager used Genie to explore CLV trends by region and cohort, excluding refunds, directly from an AI/BI dashboard. From that conversation, I captured the core logic, wrapped it into a Delta Live Table pipeline, and scheduled it as a recurring job. This reduced ad‑hoc requests by roughly 30–40% and enabled ongoing self‑serve access to CLV insights while I focused on tuning performance and data‑quality rules.
Overall, Genie helps me talk with my data in natural language, improves how quickly we uncover insights, and supports better data‑quality practices—though working across many Genie‑backed tabs can strain local hardware and sometimes slow down the workflow.
Overall, Genie helps me talk with my data in natural language, improves how quickly we uncover insights, and supports better data‑quality practices—though working across many Genie‑backed tabs can strain local hardware and sometimes slow down the workflow.
Revolutionized HR Analytics with Genie, Minor Cost Concerns
What do you like best about the product?
I really like the Genie feature on Databricks, it's great and unifies well with the ecosystem. Combining the lakehouse with Genie is simple and has transformed our HR analytics. We can ask questions in plain English about attrition and get instant, accurate responses. This effectively removes the engineering bottleneck almost completely, allowing HR to access insights directly from Genie without waiting weeks for custom dashboards. It saves the engineering team loads of hours and accelerates decision-making. Plus, setting up Databricks is seamless, as we could set up the account and start running lakehouses in minutes.
What do you dislike about the product?
Setting up Genie requires meticulous planning and data curation to get excellent responses. If the semantic model isn't perfect, it can stumble. Cost management is tricky when multiple teams use open-ended queries all day. The metric views and serverless costs features make it better, but there's room for improvement.
What problems is the product solving and how is that benefiting you?
Databricks solves ingestion, transformation, governance, and data quality challenges, offering AI and BI tools for instant insights. With Genie, HR bypasses engineering bottlenecks, saving hours and accelerating decisions. It's simple to unify lakehouse with Genie for quick, accurate responses.
Improved data governance has enabled sensitive data tracking but cost management still needs work
What is our primary use case?
What is most valuable?
The most significant benefit Databricks has brought to my company is the Unity Catalog. Previously, with our data warehouse, we weren't able to track where sensitive data was. The Unity Catalog has been a big improvement, even though we haven't gotten the rest right.
The user interface is very useful, especially in writing directly into a web interface.
From my perspective, the ability to export data effectively and use Python within Databricks are key valuable features.
What needs improvement?
I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs.
We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake.
I think introducing customer repositories would facilitate easier implementation with Databricks.
For how long have I used the solution?
I have been working with Databricks for the last six months.
What do I think about the stability of the solution?
As a platform, Databricks is fine. However, our implementation isn't particularly reliable.
We've suffered from the lack of professionals with previous experience, which makes it difficult to dig ourselves out of the situation we've found ourselves in.
What do I think about the scalability of the solution?
The scalability level of Databricks at the moment exceeds our needs. It's not a problem for us.
The sky's the limit with Databricks.
How are customer service and support?
I have addressed technical support about our issue with Databricks. It was the team that engaged with them, and I believe our development teams also reach out for support, though I'm not sure what level of support they get.
Previously, when using Snowflake, we had customer reps who were really knowledgeable and helped us to avoid beginner mistakes. With Databricks, it seems we could have benefited from similar support. Our implementation team had no experience and made obvious mistakes. It may be that we opted not to have that support, but I believe we should have.
Which solution did I use previously and why did I switch?
Before Databricks, I used SQL Server.
The big decision to switch from SQL Server to Databricks was motivated by the lack of auditing, lineage, and tracking sensitive data in SQL Server, along with a need for more flexibility.
How was the initial setup?
I did not participate in the initial setup of Databricks.
What about the implementation team?
We use a consultancy, Avanade, for our Databricks implementation. They had previously done a Databricks implementation for another part of our organization. Our implementation team lacked experience which resulted in several beginner mistakes.
What was our ROI?
So far, we're not measuring any return on investment, such as saving time, money, or resources with Databricks. We're still in the phase where our old system and the new system are running simultaneously, so everything is twice as expensive and much effort is doubled. We haven't progressed far enough yet to realize any ROI.
What's my experience with pricing, setup cost, and licensing?
I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.
I think Databricks is priced correctly. If we managed our resources better, we wouldn't be paying anywhere near that amount. The issue is with our management of resources.
Which other solutions did I evaluate?
No other options were considered because we used the consultancy Avanade, who had done a previous Databricks implementation for another part of our organization. We used them to recreate our implementation.
What other advice do I have?
I'm probably not the best person to discuss certain aspects of Databricks since I haven't explored it deeply and am not part of the team developing it.
We haven't utilized Databricks' machine learning capabilities.
From my company, data ingestion and transformation are done with Databricks, though I don't do it directly.
I don't use Databricks' features for managing data, such as data lake and warehouse operations.
Most of our current work with Databricks isn't really live yet, so measuring savings in time and money or identifying any return on investment isn't applicable right now.
I would rate this review a 7 overall.
Collaborative Notebooks and MLflow Integration Boost Productivity
What do you like best about the product?
The collaborative notebooks are quite useful. My team is able to write both Python and SQL within the same notebook, which really helps speed up our development cycle. The integration with MLflow is, in my opinion, the standout feature.
What do you dislike about the product?
There was nothing I disliked; everything worked as expected.
What problems is the product solving and how is that benefiting you?
We utilized this tool to develop predictive models aimed at optimizing our supply chain.
Databricks compute user
What do you like best about the product?
We have been using Databricks compute for our observability product. It’s super efficient and helps scale seamlessly with data
What do you dislike about the product?
Starting cost is high and makes the entry cost for our solution a little bigh
What problems is the product solving and how is that benefiting you?
Helps generate quality metrics
Great summit
What do you like best about the product?
It brings together analysts and engineers and provides value to the data for faster decision making.
What do you dislike about the product?
Frequent updates, hallucinations when it doesnt know the answer.
What problems is the product solving and how is that benefiting you?
Data Governance
New to BI/AI but growing it in out AI space
What do you like best about the product?
The upside is the ability to do everything in one platform.
What do you dislike about the product?
Need more dashboard customization on looks.
What problems is the product solving and how is that benefiting you?
Solving many prediction models such as lead scores.
Bricks is gr8
What do you like best about the product?
Alls good. Key note and everything gggggg
What do you dislike about the product?
Food is not so great would have been nbwtter
What problems is the product solving and how is that benefiting you?
Data consolidation
Feature Parity for Govcloud
What do you like best about the product?
The easy of use for big data, and the potential to use AI
What do you dislike about the product?
Gov cloud does not have feature Parity with other access levels.
What problems is the product solving and how is that benefiting you?
Solving making dashboards, queries and reports on big data
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