Databricks

Databricks is a unified data and AI platform built on Apache Spark. It combines data engineering, data warehousing (through Databricks SQL and Lakehouse), and machine learning in one workspace, so teams can ingest data, transform it, query it, and train models without moving data between systems. It is the home of the lakehouse architecture, which blends the flexibility of a data lake with the structure of a warehouse.

Key Features

• Unified lakehouse for engineering, SQL, and ML
• Collaborative notebooks in Python, SQL, Scala, and R
• Databricks SQL for fast warehouse queries
• Mosaic AI for building and serving models
• Unity Catalog for governance and lineage
• Runs on AWS, Azure, and Google Cloud

Pricing

Prices as listed in 2026 research. Always check the official site for the latest price — plans change. Free plan: No free plan — paid plans only.

How it works

The platform is aimed at data engineers and data scientists working at scale. Notebooks support Python, SQL, Scala, and R, with collaborative editing, version control, and scheduled jobs. Its AI features include Mosaic AI for building and serving models, Unity Catalog for governance, and Delta Lake for reliable, versioned data. It runs on AWS, Azure, and Google Cloud.

Databricks is priced pay-as-you-go on compute usage, with a 14-day free trial and no permanent free plan. Costs depend on the workloads you run, so check the official pricing page and calculator before budgeting. It is the strongest pick for organisations doing serious data engineering and AI at scale, but it is overkill — and expensive — for simple reporting.

Pros and Cons

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