Databricks is a unified Lakehouse for data engineering, analytics and AI, built on Apache Spark and Delta Lake, with notebooks and SQL, MLflow and Unity Catalog, on AWS, Azure and GCP.
Databricks is a lakehouse platform that fuses data engineering, analytics, and machine learning on top of Apache Spark. It offers a shared workspace—notebooks, SQL, jobs—so data teams build in one place instead of juggling tools.
You park data in cloud storage, layer Delta Lake for transactions and schema control, then spin up clusters or SQL warehouses as needed. Unity Catalog handles governance, and MLflow tracks models. Pipelines run as scheduled jobs; dashboards sit on top if you insist.
It scales without drama, handles batch and streaming in the same playbook, and makes versioned data a first-class citizen. Cross‑team workflows are smoother, and reproducibility isn’t an afterthought. If you live in Python, SQL, and parquet, it feels coherent.
Complexity creeps in fast: costs spike with sloppy clusters, and “just one more workspace” becomes sprawl. Notebooks encourage quick wins but punish long‑term software hygiene. Traditional warehouses still beat it for dead‑simple BI, and lock‑in to its patterns is real.
| Pricing category | Price | Short description |
|---|---|---|
| Community Edition | free | Limited single-user workspace for learning; small clusters; no SLA. |
| Free Trial | free | Time-limited trial with free credits; availability and credit amount vary by cloud/region. |
Prices may vary by region. We do not guarantee the accuracy of prices. For current information see: https://www.databricks.com
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