Databricks Reviews & Product Details

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.

i am working as a Data management specialist and using databricks regularly for handling data pipelines, large scale data processing, and governance tasks, i like most is that databricks provides a single unified platform for data engineering , analytics and AI , instead of using multiple tools. everything is available in one place, the lakehouse architecture is very useful because it combines data warehouse and data lake capabilities, so we can manage both structured and unstructured data efficiently. performance is very strong, especially with apache spark, it can process very large datasets quickly. i also like the collaborative notebooks where teams can work together using SQL, python or scala. Review collected by and hosted on G2.com.

one issue is that it has a steep learning curve, especially for new users who are not familiar with spark or distributed systems. cost management can also be challenging , it clustered are not optimized properly it can become expensive, sometimes too many features and configuration can makes it complex to manage for smaller teams. sometimes the platform feel complex. with many feature and configuration which can be difficult for smaller teams to manages. it it a powerful platform, but complexity and cost control are the main challenges in daily use. 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.

I like that Databricks brings everything into one place, making it unnecessary to use different tools for data processing, analytics, and pipeline work. It handles large data well, and we don't have to worry about managing clusters manually. Additionally, Databricks handles collaboration and experimentation well, making it easy to try out new things. Review collected by and hosted on G2.com.

In my point of view, the one area that can be improved is cost management. If clusters aren't monitored carefully, costs can increase faster than expected. One improvement that would help is better visibility into costs at a more detailed level. More built-in alerts or recommendations when costs start increasing unexpectedly would also be helpful. Review collected by and hosted on G2.com.

The unified platform approach is what I appreciate most. Having notebooks, data engineering pipelines, ML workflows, and SQL analytics all in one place saves a ton of time instead of juggling multiple tools. The collaborative notebooks make it easy to share work with teammates, and the cluster management has gotten a lot smoother over time. Delta Lake integration is also a huge plus for keeping our data reliable and consistent. Review collected by and hosted on G2.com.

The cost can get out of hand pretty quickly if you're not careful with cluster sizing and uptime. It's not always obvious how to optimize spending, and the pricing model feels complex. The learning curve for new team members is also steeper than I'd like, especially for people who aren't already familiar with Spark. Sometimes the UI can feel sluggish when working with larger notebooks, and debugging job failures could be more straightforward. Review collected by and hosted on G2.com.

I like that Databricks is an all-in-one powerhouse where I can do multiple works in one place. It's powerful to manage data from multiple sources and have it in a single UC to manage permissions with row-level security. I also appreciate that I can create experiments, run multiple models, and select the best one from logs, which was difficult on other platforms. Once I learned the setup, it's been easy and comfy to work with. Review collected by and hosted on G2.com.

I find it difficult to use the calculator to determine CPU serving endpoint prices because the documentation doesn't explicitly explain this. It only mentions 1 concurrency equals 1 DBU on the Azure page, which isn't clear. The pricing calculator has a single option for serving endpoints, labeled as medium with four DBU, but lacks separate options for GPU or CPU and their concurrency, making it hard to understand how it works properly. Initially, I also felt it was very tough to learn Databricks and manage deployments of workspaces, although it became easier over time. Review collected by and hosted on G2.com.

I really value how the platform brings data lakes and warehouses together into one place. It makes managing data much easier, and the SQL performance is very fast thanks to the Photon engine. I also like the collaborative notebooks because they allow me to work with both SQL and Python seamlessly in a single environment. Review collected by and hosted on G2.com.

The cost can be high, and the DBU billing system is quite complex to track. I also found that there is a significant learning curve when it comes to Spark and configuring clusters. For smaller, quick tasks, the setup time and technical overhead can sometimes feel like a bit too much. Review collected by and hosted on G2.com.