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.

What stands out most is the unified experience Databricks provides across the entire data and AI lifecycle. The ability to handle data engineering, machine learning, and AI workloads within a single platform — without constantly switching tools — is a significant productivity gain. The ML pipeline capabilities in particular are notably fast and intuitive, allowing our team to iterate quickly. The seamless integration with cloud infrastructure and the collaborative notebook environment have also made onboarding and cross-functional work much smoother. Review collected by and hosted on G2.com.

As a recent adopter, the main challenge has been the learning curve associated with platform administration and cost management. Understanding and optimizing cluster configurations, compute costs, and Unity Catalog governance requires a level of expertise that takes time to develop. Documentation is extensive but can sometimes be overwhelming for teams that are just getting started. A more guided onboarding experience for new enterprise customers would be a welcome improvement Review collected by and hosted on G2.com.

Databricks works well when it is treated as an enterprise platform, not just a notebook environment. For a large wealth manager, it has enabled a firm-wide lakehouse spanning analytics, regulatory reporting, automated high-net-worth collateral generation, data reconciliation, and enterprise data mesh.

The strongest feature is Unity Catalog. It makes data mesh operational by giving domain teams a governed way to publish, discover, secure, and consume trusted data products. Lakeflow Spark Declarative Pipelines and Lakeflow Connect have also been valuable for ingesting data from hundreds of source systems into governed Delta tables, with built-in quality expectations reducing custom validation work.

For operational workloads, Photon, Job Clusters, Instance Pools, Databricks Workflows, and Databricks Asset Bundles have been especially useful. They allow teams to run repeatable, right-sized pipelines without maintaining DIY Spark infrastructure. One reconciliation framework now processes hundreds of millions of rows daily at materially lower compute cost than a VM-based Spark approach. Business impact includes faster time to insight, fewer manual reporting touchpoints, faster regulatory response, reusable data quality patterns, and a practical federated ownership model. Review collected by and hosted on G2.com.

Cost management is a watchout. Photon, Job Clusters, and Instance Pools can lower costs dramatically, but only when workloads are well-designed. Poorly tuned jobs, idle clusters, oversized compute, or duplicated pipelines can still burn budget fast. Finally, the platform creates some ecosystem dependency: once reconciliation, ingestion, orchestration, governance, and reporting patterns are Databricks-native, switching costs rise. In short, Databricks is a strong enterprise platform, but it rewards disciplined engineering and punishes casual adoption. Review collected by and hosted on G2.com.

Databricks brings data engineering, analytics, machine learning, and AI workflows together in one platform, making collaboration much easier across teams. I especially value the flexibility of working with SQL, Python, and notebooks in the same environment, along with the scalability of Spark for large datasets. Features like Delta Lake, Unity Catalog, and the growing AI capabilities have helped streamline development, improve governance, and accelerate time to insight. Review collected by and hosted on G2.com.

While Databricks is a very powerful platform, the learning curve can be steep for new users, especially those without a distributed computing background. Some features and UI experiences can feel fragmented or evolve quickly, making it difficult to keep up with best practices. Cost management and cluster optimization also require careful monitoring to avoid unexpected expenses, particularly for teams new to the platform. Review collected by and hosted on G2.com.