Databricks Data Intelligence Platform

What differentiates Databricks Data Intelligence Platform from other similar products?

What sets Databricks apart is how well it brings data engineering and machine learning together in one place. Instead of juggling separate tools for storage, processing, and model development, everything runs smoothly within a single environment. Features like Delta Lake and the collaborative notebooks make it much easier for teams to work on the same data without running into version issues or performance problems.

What is your favorite aspect of this product?

My favorite thing about Databricks is how quickly it can process large datasets. Even complicated transformations run fast, and the platform handles scaling automatically, so I don’t have to worry about managing servers or clusters manually. It makes big data work feel much more manageable.

What do you dislike most about this product?

The part I struggle with the most is the cost. It’s easy for expenses to climb if jobs aren’t optimized or if clusters run longer than expected. The interface can also feel overwhelming for new users, especially those without a strong technical background.

What recommendations would you give to someone considering this product?

I’d recommend taking time to plan how you’ll manage compute and permissions from the start. Databricks is incredibly powerful, but you get the most out of it when you set clear job schedules, monitor usage, and train your team on basic optimization. If you handle those pieces early, the platform delivers a lot of value.

What differentiates Databricks Data Intelligence Platform from other similar products?

Combines data engineering, data science, and analytics workloads in one scalable platform—supporting structured, semi-structured, and streaming data seamlessly.

What is your favorite aspect of this product?

Its unified development environment—empowering both IT professionals and business analysts to collaborate using shared tools and workflows—significantly boosts productivity and aligns multi-disciplinary teams under a cohesive platform.

What do you dislike most about this product?

Databricks can be extremely expensive, especially when clusters are left running and not auto-terminated

What recommendations would you give to someone considering this product?

Adopt auto-termination clusters with workload-aware sizing and regularly review job runtimes, enable Spot/Preemptible instances when appropriate, use Unity Catalog and job isolation for governance, and set cost alerts in the Databricks Cost Management dashboard to prevent runaway spending.

What differentiates Databricks Data Intelligence Platform from other similar products?

The pioneering of Lakehouse architecture.

What is your favorite aspect of this product?

The fact that data engineers can build reliable ETL, data analyst can run high performance SQL and data scientists/engineers can build and deploy complex data, all on the same dat, without costly, complex and error-prone movement or duplication

What do you dislike most about this product?

Cost optimization and predictability

What recommendations would you give to someone considering this product?

Prioritize unity catalog for data governance.