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.

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.

I really appreciate the Databricks Unity Catalog for enforcing data policies, as it’s ready to use and offers enterprise-grade security that helps our data lakehouse with all access controls and compliance. It's a one hub solution, especially valuable for organizations that are tightly governed, like us. We also use Spark as a service, Phoenix, and Fortran Gemini quite a lot, which makes it a perfect solution for our data analytics and data hub. The initial setup was pretty straightforward, involving some architectural discussions with the Databricks team, and it went smoothly over a few months. Review collected by and hosted on G2.com.

When acquiring larger data sheets, we often see problems. We are still looking to better refine our data access patterns for end users, especially on bigger datasets which are heavy compute intensive. This makes interactive queries and what-if analysis take quite a bit of time, making them not so interactive for the end users. Review collected by and hosted on G2.com.

Databricks has fundamentally changed how organizations approach Data Engineering, Machine Learning, and AI. What began as a strong analytics platform has grown into a unified ecosystem that lets teams build, govern, and scale data-driven solutions within a single environment.

From a Data Engineering standpoint, Databricks streamlines the creation of modern data lakes and lakehouse architectures. Capabilities such as Delta Lake, Unity Catalog, and automated pipelines reduce operational complexity while strengthening data quality, governance, and overall reliability.

For Machine Learning teams, Databricks offers an end-to-end workspace where data scientists, engineers, and business stakeholders can collaborate smoothly. With experiment tracking, model management, feature engineering, and scalable training, it shortens the path from early ideas to production-ready outcomes.

Most impressive is Databricks’ pace of innovation in AI. The platform has positioned itself at the center of the Generative AI wave by bringing large language models, vector search, AI agents, and enterprise-grade governance directly into the Lakehouse architecture. This helps organizations move beyond experimentation and deploy AI solutions securely and at scale.

The consistent, unified experience across Data Engineering, Analytics, Machine Learning, and AI makes Databricks feel like a strategic platform rather than just another tool. It helps break down silos, speed up innovation, and turn data assets into real business value.

Databricks isn’t simply participating in the future of data and AI—it’s helping shape it. For organizations aiming to modernize their data platform and build enterprise AI capabilities, Databricks stands out as one of the most compelling options on the market today. Review collected by and hosted on G2.com.

Nothing stands out for me at the moment, aside from the diversity in how the world’s population is represented. 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.

What I appreciate most about Databricks is its ability to unify data engineering, data science, and analytics on a single platform. The collaborative environment—especially the notebooks and integrated workflows—makes it much easier for teams with different skill levels to work together without constant context-switching.

Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful. Review collected by and hosted on G2.com.

Cost management is one area that could be improved. While Databricks offers autoscaling and flexible cluster options, it’s easy for resource usage to escalate unexpectedly, especially with large datasets and long-running jobs. Keeping costs predictable often requires careful oversight and a solid understanding of the platform’s pricing model.

Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error. Review collected by and hosted on G2.com.