I appreciate Databricks for its intuitive interface, which is very user-friendly and flexible, making work comfortable. I use it extensively for collaborative work; my colleagues can view, comment on, and improve analyses all in one place, which is really handy. I can also develop production models, train, test, and deploy them efficiently. It offers excellent data control by allowing me to manage who accesses which data and track changes, reducing technical issues and data inconsistency problems while improving regulatory compliance. I also found the initial setup simple, which is a big plus for us. Review collected by and hosted on G2.com.

I don't like the idea that scalability can process large amounts of data and generate resources as needed. Furthermore, it requires better governance at scale and demands a significant organizational effort. What I mean is that it requires much more effort than normal, and that's something that should be changed and optimized to avoid so much effort. Review collected by and hosted on G2.com.

I must say that Databricks has earned a place among my favorites since I started using it. It changed the way I handle large volumes of data and is truly excellent. I use several of its features to divide tasks and run operations in parallel, allowing us to keep up with business demands. Databricks solves problems, keeps everything under control, and manages data quality, enabling us to work much more efficiently. I enjoy being able to work on projects with my colleagues in real time, sharing notebooks and conducting joint analyses. I love the speed at which large amounts of data can be processed, allowing me to obtain information more quickly and make informed decisions. The initial setup was great as well. Review collected by and hosted on G2.com.

Personally, I would like to see a more intuitive interface and some features that could benefit from greater simplicity. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

What I like most about Databricks is its unified platform for data engineering, analytics, and machine learning. It makes working with large datasets much easier and allows different teams to collaborate in a single environment. I especially like the notebook-based workspace, its scalability, and its integration with Apache Spark. Review collected by and hosted on G2.com.

What I dislike about Databircks is that the platform can come with a learning curve for new users, especially when you’re dealing with advanced configurations and large-scale data pipelines. Managing compute resources and keeping costs under control can also take careful monitoring and ongoing attention. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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, Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.