Databricks Reviews

“has great features for developers”

Pros

Databricks really helps developers tackle common challenges with features such as Genie, Lakeflow Connect and DLT.

Cons

Before getting started, I'd want a clearer understanding of the compute model, pricing and the right way to use the platform. It definitely feels like there's a lot to learn upfront.

“A scalable all in one tool”

Pros

This tool has been excellent for scalability and for bringing data engineering, analytics and machine learning into one unified environment. It helps me work through large datasets efficiently while keeping everything organized within a single platform. That scalability is especially important because it supports increasing data volumes and more complex workloads without causing performance problems. As projects grow the platform is able to expand resources effectively to keep up.

Cons

There is still a learning curve with some features, particularly for newer users dealing with advanced configurations or cluster management. Certain parts of the interface could feel more user friendly as well. And yes getting started with the core features was fairly easy but more advanced areas such as cluster optimization, permissions and integrations took extra time and a stronger technical background to set up properly.

“must have tool”

Pros

The software does a really nice job of bringing data engineering, analytics and machine learning together in one unified platform. The end to end data flow is much faster, having a single source of truth is a big advantage and collaboration across teams is noticeably better. Getting everything set up at the beginning was also fairly easy.

Cons

One thing that does stand out is cost management. Since it's a scalable platform that relies heavily on compute, expenses can climb pretty quickly.

“Powerful performance”

Pros

Fast, dependable data processing and SQL performance are a big plus and the lakehouse platform does a really good job of bringing everything together in one place for our team.

Cons

Getting comfortable with it takes time because the learning curve is pretty steep. It expects strong coding ability and Spark knowledge so it can feel like too much for teams that just need basic SQL reporting.

“AI boost win”

Pros

What I like most about Databricks is having data engineering, analytics and AI workflows all in one shared platform as it makes collaboration much easier. It is especially useful when working with large datasets and notebooks and it helps build the right pipelines without the headache of juggling too many separate tools.

Cons

Keeping track of costs and resource usage can be difficult especially once more teams, clusters and jobs begin using the platform. Permission management also needs better synchronization. I'd also like clearer troubleshooting when a job fails, along with a more polished experience for workspace governance and configuration. On top of that CDC lake flow keeps getting stuck on the last table without giving any clear insight and serverless logs can be very hard to follow which makes figuring out job failures more difficult.

“decent platform”

Pros

Having data engineering, analytics and machine learning all in one platform makes the workflow feel smooth and able to grow as needs increase.

Cons

The cost can be on the higher side and there's definitely a bit of a learning curve when you're just getting started.

“Great tool it is”

Pros

It is a big win for us as having data engineering, SQL analytics and machine learning together is proven highly beneficial. My team can prepare data, run SQL queries and build models in one shared workspace without having to switch between tools or lose track of what we're doing.

Cons

Costs can climb pretty quickly and the cluster startup lag sometimes feels like waiting around for a coffee machine to finish heating up.

“Powerful all-in-one hub”

Pros

Databricks is proven useful because it provides everything in in one shared workspace. So it removes the need to switch between different tools for separate tasks which makes the whole workflow feel much more connected and efficient.

Cons

The pricing model based on Databricks Units can be costly and hard to predict in advance. Expenses can rise fast especially when auto-scaling clusters are running so keeping costs under control can be a challenge.

“Excellent lakehouse capabilities”

Pros

One of the biggest advantages is being able to bring data warehousing and data lakes together in a single lakehouse setup. That lets me handle BI and data engineering in the same platform instead of piecing together several different tools. The AI features also help speed up SQL writing and improve query execution which is a real plus.

Cons

Unity Catalog offers a lot of power but configuring fine-grained access control across data, schemas and workspaces can get complicated especially at larger companies. The UX/UI also seems a bit uneven as some areas look polished, while others still seem less refined.

“tool that delivers”

Pros

Bringing everything into one platform has made a real difference for our team. We can work together in shared notebooks and Delta Lake's ACID transactions have helped keep our data consistent across projects. Auto Loader has easily been the standout feature for us since it picks up new files as soon as they arrive in cloud storage which saves us around 2 to 3 hours every week that used to go into manually checking pipelines. Unity Catalog also cleaned up a big headache by giving us one central place for governance and access control instead of the messy setup we had before. On top of that getting started was fairly easy as we had our first cluster running and notebooks connected to S3 within a day which felt impressive for a platform with this much capability. The workspace setup and cloud integration documentation are also well put together and helpful to follow.

Cons

One thing that still throws us off is cluster startup time. A cold start usually takes about 3-5 minutes and that becomes annoying during iterative debugging when all you want to do is test a small fix quickly. Cost management could also use more work. The billing dashboards are getting better but right now it still takes extra digging to figure out exactly which job or user is responsible for driving costs up.

“Fast PySpark with one-click CI”

Pros

Databricks has been proven the best for both real-time data ingestion and processing as well as batch workloads. Using it with PySpark is very natural and having one platform that handles both streaming and batch processing is a big advantage. The in-memory processing cuts processing time down significantly and dataframes make structured data much simpler to work with. Execution is fast and I can clean, transform and manipulate data without leaving the same environment. Deployment is easy too and the one-click CI pipeline is something I genuinely like. Getting started was pretty simple and the product support made the initial setup feel effortless.

Cons

I'd still want to see an integrated agentic framework so it can become a true one-stop platform for both Data and AI.

“Clean UI with quick ramp-up”

Pros

Compared with other providers, the UI seems much better designed. Also it is easy to work in and the learning curve progresses in a clear, manageable way.

Cons

The usage-based pricing can get pretty expensive and it may be challenging for people who aren't comfortable with Python or Spark.

“Great analytics hub”

Pros

Having data engineering, analytics and machine learning in one place is what makes Databricks really valuable to me. It brings everything together on a unified platform, makes scaling workflows much more manageable and handles big data without much trouble. Team collaboration also is a lot more organized which is something I genuinely like.

Cons

One area that could be better is cost transparency. User based pricing isn't always clear so it can be difficult to predict expenses. The initial setup and cluster configuration also come across as fairly complex and better documentation around those parts would help. On top of that some areas of the UI could be more user friendly.

“we really liked it!”

Pros

Building ETL pipelines and handling large scale data with Spark has been very effective with Databricks. The biggest advantage is how well it works with Apache Spark, along with the collaborative notebooks and the way it brings large-scale data processing into one unified platform. Its Spark integration allows huge datasets to be processed quickly without the hassle of managing cluster setup and the shared notebooks make real-time teamwork much more practical. The scalable architecture also holds up well under heavy data workloads and getting started was fairly easy especially thanks to the cloud integration.

Cons

The interface can feel somewhat cluttered from time to time, cluster startup can be slower than expected and the pricing may become expensive for smaller projects or longer-term use.

“decent tool overall”

Pros

Having data engineering, processing and analytics all in one place is the biggest advantage of Databricks. It makes building and managing scalable Spark pipelines much more manageable without needing to spend too much time worrying about the underlying infrastructure.

Cons

Keeping costs under control with it can be challenging when clusters are not managed carefully. On top of that debugging distributed jobs is not always simple and the UI can feel a little heavy when all you need is a quick look at the data.

“Strong Spark performance”

Pros

Handling large-scale data with Spark has been one of the biggest advantages for me. The collaborative notebooks make teamwork much more practical and the integrations with AWS and BI tools help keep the entire data pipeline efficient and well connected.

Cons

Pricing can get expensive depending on how much you use it and certain parts of the interface especially cluster and job setup, are not very user friendly in the beginning. There's also a fairly steep learning curve for new users.

“all in one software”

Pros

Databricks stands out for its powerful, all in one analytics ecosystem. Unity Catalog and Metastore make governance and access management pretty easy and the Lakehouse architecture brings together the strengths of both data lakes and warehouses. PySpark support, dbutils and shared workspaces help development move faster, while serverless compute makes scaling much simpler without the usual infrastructure burden.

Cons

One frustration is how long all-purpose clusters can take to start which can break your momentum and slow down work. Git integration is also somewhat slow at times especially when committing or syncing so version control isn't as smooth as it should be.

“Unified platform for CI/CD”

Pros

Having data engineering and data science in one unified platform really cuts down the friction between teams and makes it much faster to build and deploy solutions. Databricks is particularly strong when it comes to supporting end-to-end CI/CD pipelines.

Cons

An area that could definitely be improved is cost management since making the platform more cost-efficient would benefit users a lot. On top of that the operational side can feel fairly complex at times which makes the platform harder for new users to navigate. If those issues were addressed, it would be much more approachable for engineers to work with.

“collaborative lakehouse boost”

Pros

Databricks has been a strong fit for us because the collaborative lakehouse setup really helps bring our data engineering and machine learning work into one place. It has done a lot to close the gap between the engineering and analytics teams since we can handle both BI and AI from a single platform. Another nice surprise was how quickly we were able to get started from a workspace standpoint especially thanks to the native Azure integration.

Cons

The platform can be tough for non-engineers to pick up so the learning curve is definitely noticeable. We also have to stay very disciplined about tracking costs because auto-scaling clusters can drive up expenses faster than expected if they are not managed carefully.

“Powerful for data teams”

Pros

Working in data management, I use Databricks regularly for data pipelines, large-scale processing and governance work. The part I appreciate most is that it brings data engineering, analytics and AI into one unified platform instead of making us rely on several separate tools. Having everything in one place is very practical. The lakehouse architecture is especially valuable because it blends the strengths of a data warehouse and a data lake which helps us handle both structured and unstructured data efficiently. Performance is also very strong, particularly with Apache Spark and it processes very large datasets quickly. The collaborative notebooks are another big plus since teams can work together using SQL, Python or Scala.

Cons

A noticeable downside is the steep learning curve especially for people who are new to Spark or distributed systems. Managing costs can also be tricky because if clusters are not optimized well, expenses can rise quickly. On top of that the large number of features and configuration options can make the platform feel complicated, particularly for smaller teams. It is definitely a powerful solution but day-to-day use can be challenging when it comes to complexity and keeping costs under control.

“its a complete tool”

Pros

In real project work, it makes a huge difference not having to jump across multiple tools for data engineering, analytics and machine learning. The collaboration side is also a standout. The notebooks feel fluid and interactive so teams can work together without things turning chaotic. For serious data projects, it almost feels like Google Docs for large-scale data work. Another thing I appreciate is how well it handles big volumes of data without making the experience feel overly complicated. Even with large datasets, the platform stays approachable and can scale up when needed. From an AI and ML perspective, it also fits very naturally. You can build,

Cons

It seems a bit overwhelming at first. There are clusters, notebooks, jobs and workflows so a lot is happening at once and if you're new to the platform, it takes time to understand how all the pieces connect. Managing cost is another downside. The platform is undeniably powerful but spending can climb fast if cluster usage or auto-scaling settings are not watched carefully. It really takes discipline and regular monitoring to keep costs under control.

“unified data platform”

Pros

This tool makes data management much simpler. I used to rely on multiple tools for different data tasks and that setup was far less convenient, while here everything is connected. The notebook functionality is another major benefit especially when working with PySpark and that is something I really value. It also lets me make updates and changes quickly without needing too much setup beforehand. Collaboration has improved as well since my team can work on their own projects at the same time while still keeping track of overall progress. Version control can feel a little unclear at times, though. Performance wise, this tool has been very efficient in handling big data and generally runs without noticeable delays. Automatic cluster scaling saves both me and my team time on the infrastructure side since we do not have to spend extra effort planning or adjusting resources. There are a few minor UI slowdowns now and then but overall because it is so effective for implementation and integration, it keeps me coming back to it regularly.

Cons

One thing that frustrates me is the UI. After spending more time in the platform, switching between notebooks and clusters can start to feel irritating. Cost management is another issue because expenses can pile up quickly if you are not paying close attention. Clusters sometimes keep running longer than needed without me or my team noticing which drives project costs up unnecessarily. Debugging can also be quite complicated especially when dealing with more complex pipelines since it often takes extra time and effort to figure out exactly where something went wrong. Customer support has also been inconsistent at times which can lead us into situations that are more frustrating than helpful.

“Much faster content creation”

Pros

It's helped our team produce content more quickly.

Cons

I noticed that the product review and approval process at our company has been taking a long time.

“Best Spark processing and sharing”

Pros

Working with Databricks for big data processing and data engineering in PySpark has been really effective. It lets me handle terabytes of data without issues thanks to the Spark architecture. One feature I really appreciate is Unity Catalog and its access framework because it makes sharing data across the organization much more manageable while still letting me control permissions like Select, View and other access on Delta tables by role or team. The setup at the beginning went very well and the integration with Microsoft Fabric is something I have also valued.

Cons

The area that could use some improvement is the billing experience especially since I am using it through Azure.

“all in one data workspace”

Pros

It really stands out by bringing data engineering, analytics and machine learning together in one unified workspace. Shared notebooks make teamwork much more convenient and the integration with big data tools helps cut down on time. It makes complicated workflows easier to manage while still giving me the advanced capabilities I need when the work gets more demanding.

Cons

It does have a few downsides especially when it comes to cost. It can feel pretty expensive for smaller teams or projects and cluster setup along with cost management can be a little tricky to handle. The interface is powerful but it can also feel overwhelming if you're new to it and troubleshooting distributed jobs is not always as simple as I would prefer.

“Its a unified data platform”

Pros

Databricks does a really good job of simplifying big data work by bringing data engineering, analytics and machine learning into one unified platform. The tight Spark integration and strong scalability make working with large datasets much more efficient.

Cons

Pricing can climb pretty quickly with heavy usage especially when clusters aren't optimized well. On top of that debugging and monitoring jobs can feel a bit less user friendly than with more traditional tools.

“effective lakehouse for analytics”

Pros

It is a powerful platform that combines data engineering, AI/ML and SQL analytics in one collaborative workspace.

Cons

This tool is expensive which can add up quickly with frequent cluster usage and workloads that aren't well optimized.

“Powerful workspace and cluster control”

Pros

Databricks stood out for how manageable it is. The cluster management, unified workspace, performance optimization and versioning have all been extremely valuable in day to day use. Having all the tools available in one console is really convenient especially for large-scale data engineering work. The initial setup was also very easy which made getting started with the platform pretty pleasant.

Cons

There really isn't much to complain about.

“Unified workspace boosts team speed”

Pros

Having data engineering, analytics and machine learning in a single, well-organized workspace is the biggest advantage of this tool. It cuts down on wasted time, makes collaboration much simpler and helps teams work faster when dealing with large volumes of data.

Cons

One part that gets frustrating with it is Auto Loader when source data keeps changing often especially if column names or data types shift unexpectedly. For instance a field such as customer_id might suddenly appear as cust_id or a column that used to be a string may start coming through as an integer, creating schema drift and disrupting downstream processing. It is also inconvenient when schema inference is not completely accurate, particularly with nested JSON or other semi-structured data because that leads to extra manual fixes and ongoing maintenance just to keep pipelines running properly.

“An all in one data powerhouse”

Pros

This tool is amazing as having it means we can do data processing, analytics and pipeline work without jumping between many separate tools. It works very well with large amounts of data and not having to manually manage clusters saves a lot of time. The platform also handles collaboration and experimentation well so testing new ideas is easy and practical.

Cons

The part that still needs improvement is cost management. If cluster usage is not watched closely, expenses can climb quicker than expected. It would be much better to have more detailed visibility into where costs are coming from. Built-in alerts or recommendations when spending starts rising unexpectedly would also make a big difference.

“we rely on it for a lot”

Pros

Databricks sits at the center of our day to day data work and we rely on it to build and run pipelines across a full medallion architecture, starting with data extraction from SAP and Arkieva through to datasets that are ready for reporting. The notebook environment is also user friendly, the platform offers a huge range of capabilities and Asset Bundles have really strengthened our CI/CD process with Azure DevOps. Its cloud integrations came together without much friction and once everything is configured, it tends to run reliably. There is definitely a learning curve for newer teammates especially when they start dealing with Unity Catalog and DABs and costs can rise if cluster settings are not watched closely. Even so support has been decent and the documentation is good enough that we rarely need to submit tickets. All in all it is a very capable platform that covers a lot in one place and it would be hard to picture our data engineering workflow without it.

Cons

Costs can climb pretty quickly if cluster sizing and job configurations are not managed carefully so keeping the environment optimized takes ongoing attention. On top of that newer team members usually need time to get comfortable, particularly with Asset Bundles, Unity Catalog and setting up the CI/CD components correctly.

“productivity boost !!”

Pros

It completely changed the way our team manages end to end data workflows. Several things really stand out. The notebook UI is user friendly and the SQL editor is polished so moving between Python, SQL and Scala in the same workspace cuts down a lot of context switching. On the integration side the native connections to Azure, Unity Catalog and Delta Sharing save us from spending so much time on setup and plumbing. Lakehouse Federation was also a nice surprise since we can query external sources without having to move the data first. Performance has been impressive too. Delta Lake's auto-optimization and liquid clustering made a noticeable difference in our query speeds and Photon has been a huge win for heavy aggregations and near real-time dashboards. The DBU pricing model takes a little time to understand but once we consolidated our data warehouse, ETL and ML tools into it, our overall infrastructure costs dropped quite a bit. Onboarding has also been faster thanks to Databricks Academy and the built-in documentation and the community forum has been genuinely helpful for more niche questions. The AI features are another plus as Genie helps business users ask questions in plain English and get reliable answers which has clearly reduced ad hoc requests to our data team, while Databricks Assistant in notebooks speeds up both code generation and debugging.

Cons

Nothing to share