Ease of working with data, with scalability and high processing power. It has many integrations, which facilitates access to data from different sources. Furthermore, it is natively prepared for AI.

many features with intuitive ui, great ai assistance and you easily integrate with other services. I like the facts that yo can orchestrate jobs and notebooks easily, run queries on large volumes of data and the fact that the AI genie can help you a lot understand and implement better and faster tasks.

I've been using Databricks as part of our data engineering workflow to build and maintain ETL pipelines, analyze large datasets, and support reporting requirements. One of the things I like most is that it brings data engineering, analytics, and notebooks into a single workspace. Instead of switching between multiple tools, I can write PySpark code, validate transformations, collaborate with teammates, and schedule jobs from the same platform. This has made day-to-day development more organized, especially when working on multiple data pipelines. Another feature I rely on frequently is the notebook environment. It's convenient for developing and testing transformations before moving them into production. During development, I often use notebooks to inspect sample data, troubleshoot failed transformations, and validate business logic with SQL and PySpark. The ability to mix code, markdown documentation, and query results in one place also makes it easier for team members to understand the implementation during code reviews or knowledge transfer sessions. I also appreciate the platform's scalability. Some of our data processing jobs involve millions of records, and Databricks handles distributed processing efficiently without requiring us to manage the underlying infrastructure directly. Features like cluster management, job scheduling, and integration with cloud storage reduce operational overhead. That said, cluster startup times can occasionally delay quick debugging sessions, and managing compute resources carefully is important to avoid unnecessary costs. Overall, Databricks has helped simplify large scale data processing while giving enough flexibility for both development and production workloads.