My main use case for Databricks is running SQL queries. I use Databricks in my day-to-day work by doing SQL queries directly in Databricks using the Genius platform to better correct the queries instead of doing queries on another platform.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
792 reviews
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External reviews are not included in the AWS star rating for the product.
Web-based SQL workflows have become more secure and have saved significant query time
What is our primary use case?
What is most valuable?
The best features offered by Databricks include the fact that it is on the web, that it does not depend on installing any software, and most importantly, the security that prevents connection to anyone else who is not logged in.
Regarding the security and web access I mentioned, I have noticed concrete benefits related to collaboration and data protection within my team, such as it being very secure and the fact that every time we enter the platform, it does the same credential verification.
The features of Databricks have impacted my organization positively, as it has done so very efficiently since we switched from several platforms to using this one. After implementing Databricks in my organization, I have observed that it has been more efficient with my team.
What needs improvement?
I think the aspects of Databricks that should be improved are that it could be faster and that I would like to be able to run direct queries from the server. I have not seen any other improvements that I think are needed in Databricks.
What do I think about the stability of the solution?
Databricks is stable.
What do I think about the scalability of the solution?
I rate the scalability of Databricks as excellent.
How are customer service and support?
Databricks customer support is very good. I would give Databricks customer support a rating of ten.
Which solution did I use previously and why did I switch?
I did not use any other solution before Databricks.
What was our ROI?
I have seen a return on investment, as time is greatly saved and processes are faster.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, implementation costs, and licensing is that it is very efficient and very fast.
What other advice do I have?
My advice to others considering using Databricks is that it is the best platform with artificial intelligence. I give this review an overall rating of ten.
All-in-One Delta Lake Platform That Makes ETL Fast and Cost-Efficient
What do you like best about the product?
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 the product?
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 the product 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.
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.
User-Friendly, Affordable Data Processing at Scale with Fast Support
What do you like best about the product?
It has a user-friendly interface and integrates with other clouds easily. We can process TBs of data without much effort. Compared to other data-processing tools, its price is lower. It also includes Ginee AI, and by using that we can handle data processing much more easily. If we face any issues, they solve the problem in less time.
What do you dislike about the product?
it's is very difficult to use for new users
What problems is the product solving and how is that benefiting you?
i'm a data engineer so i'm using this for process the TB's of the data. for ML Flows. integrate with data science, analytics team
Databricks Boosts Productivity with a Unified Workspace and AI-Assisted Development
What do you like best about the product?
As an ADE, what I like most about Databricks is that it removes infrastructure friction, so I can focus purely on data engineering logic. I also really appreciate the unified workspace: I can write PySpark for data extraction and transformation, switch to SQL for exploratory analysis, and review data lineage, all within a single browser tab is a huge productivity boost. On top of that, the built-in AI features have been incredibly helpful because they let me worry less about syntax and spend more time on the logic itself. Finally, with the seamless integrations through Lakehouse Federation and the straightforward onboarding, my work has become much smoother.
What do you dislike about the product?
While the platform is excellent for development, the DBU consumption model and cluster management can feel a bit daunting at my level. As a beginner, I spent a lot of time testing different bits of logic, and it was easy to forget to terminate the all-purpose cluster afterward, which led to minimal but still unnecessary credit consumption. Thankfully, auto-termination exists and helped keep credits from disappearing. Still, a more aggressive auto-termination setting or a smarter pause feature would make it easier to avoid any credit loss in the first place.
What problems is the product solving and how is that benefiting you?
Databricks helps solve the local environment hell-hole that often slows down junior engineers. By providing a ready-to-use Lakebase architecture, it lets me practice enterprise-level data engineering without needing to connect to VPNs or deal with complex Docker setups. In my project, it addressed the full flow: ingesting raw data, transforming it, and serving it for analytical queries. This also benefits my team, because I can onboard onto real data pipelines much faster and start contributing sooner. At the same time, I’m learning how to build production-ready ETL workflows without my senior teammates having to spend hours helping me troubleshoot my local Python/Spark environment. An unexpected benefit was how seamless collaboration is. Because the notebooks are cloud-based and ties to the workspace, sharing my project with senior engineers for code reviews was as simple as sending a link. Additionally, the way Databricks handles metadata made me realize early in my career how important data governance is.
Centralized Governance, Powerful Migration Tool
What do you like best about the product?
I like the Unity Catalog as a single governance layer which centralizes access control and offers fine-grained permissions across data assets. The workspace API and automation features are valuable for streamlining operations. I appreciate that volumes replace mounts, improving security with credential-free access. The Lakehouse Federation simplifies cost consolidation and reduces data movement costs. Having Photon and ML Runtime on the same platform enhances operational efficiency. The initial setup was user-friendly, thanks to the guidance from the Databricks portal.
What do you dislike about the product?
* Migration tooling is manual and fragmented * Mount-to-Volume path conversion has no automated path * Cluster security mode NONE still exists * Hive metastore and UC coexist awkwardly * Custom WHL libraries on mount lack a clean upgrade path
What problems is the product solving and how is that benefiting you?
Databricks provides centralized access control with fine-grained permissions, identity-based access without exposing storage credentials, unified data discovery and lineage, and reduces operational overhead by consolidating platforms and managing data more efficiently.
Lakehouse Unifies Ad and CRM Data for Governed, SQL-Powered ML Segmentation
What do you like best about the product?
The lakehouse approach allows our team to bring together the ad platform data along with the CRM information in a single governed space. This saves on switching between tools while analyzing and building audience segments using ML by running SQL queries.
What do you dislike about the product?
The technical complexity involved in setting up the clusters may pose a challenge for non-technical marketers.
What problems is the product solving and how is that benefiting you?
It eliminates manual reporting via spreadsheets and allows me to gain insights on paid social ROAS in real time and helps in making quick decisions on reallocating budgets for Meta and LinkedIn campaigns.
Scalable Unified Analytics with Fast Big Data Processing and Strong Spark Integration
What do you like best about the product?
Scalable data processing, unified analytics platform, fast big data handling, collaborative notebooks, strong Spark integration, efficient data engineering workflows.
What do you dislike about the product?
Complex pricing structure, steep learning curve for beginners, cluster management can be confusing, sometimes expensive for smaller teams
What problems is the product solving and how is that benefiting you?
Handling large-scale data processing, unifying data engineering and analytics, faster ETL workflows, collaborative data science, scalable machine learning operations
Making data systems less messy with a unified Lakehouse approach
What do you like best about the product?
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.
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 the product?
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.
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 the product 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.
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.
Genie’s Quick Updates and Releases Make It a Joy to Use
What do you like best about the product?
I love Genie and the quick update/release
What do you dislike about the product?
Honestly, I don’t really have anything to dislike, lol.
What problems is the product solving and how is that benefiting you?
We are currently building a semantic layer using Genie and the agent. I like how easy and structured Databricks is to help with that.
Databricks centralizes data, analytics, and AI
What do you like best about the product?
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.
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 the product?
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.
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 the product 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.
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