The unified platform experience is what keeps me on Databricks. Having notebooks, pipelines, SQL warehouses, ML, and governance all in one place under Unity Catalog means I’m not constantly stitching together five different tools just to get work done. Lakeflow Pipelines (formerly DLT) makes it straightforward to build medallion-architecture pipelines, and the Photon engine delivers real performance gains on SQL workloads without requiring any code changes. Recent additions like Genie Code and background agents also show they’re serious about agentic AI—it doesn’t feel like a bolt-on copilot, because it can actually understand your data context through Unity Catalog. Serverless compute has been another big quality-of-life improvement as well, since I no longer have to wait for cluster spin-up when I just want to run quick, ad hoc queries.

The ability to converge data engineering, data science, and analytics on a single platform without compromising on governance, performance, or flexibility is still rare in the industry. Databricks executes this exceptionally well.

What I like most about Databricks is how it brings data engineering, analytics, and machine learning together in one platform. It streamlines the entire data pipeline—from ingestion and transformation through to serving—so I don’t have to rely on multiple separate tools to get end-to-end workflows done. Its integration with Spark and Delta Lake is another big plus, making it both scalable and dependable when working with large datasets.