Lakehouse platforms & table formats · Databricks, Inc.

Databricks

Managed lakehouse platform, built by Apache Spark's creators, combining Spark-based compute with governance and ML tooling.

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Databricks is a commercial cloud platform, founded by the original creators of Apache Spark, built around a "lakehouse" architecture that combines data-lake storage with warehouse-style management and performance. It runs Spark (and its own SQL engine, Photon) for large-scale ETL, SQL analytics and machine learning, adds Unity Catalog for governance and data lineage across workspaces, and manages the underlying tables using the open-source Delta Lake format. Databricks runs on AWS, Azure and GCP, provisioning and autoscaling clusters on the customer's own cloud account, and bills usage in Databricks Units (DBUs) that vary by workload type and compute tier, on top of the underlying cloud infrastructure cost. It targets organizations that want one platform spanning data engineering, SQL analytics, and ML/AI rather than assembling Spark, a catalog and a warehouse separately.

At a glance

Vendor Databricks, Inc.
Pricing model Usage-based
Free tier Yes
Deployment Cloud
Open source No
Best for Organizations wanting a single managed platform spanning data engineering, SQL analytics and machine learning on Spark.

Pricing

Pay-as-you-go pricing metered in Databricks Units (DBUs) per second, varying by workload type and tier, plus separate underlying cloud infrastructure costs; committed-use contracts offer discounts. A limited free Community Edition exists.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • Managed Apache Spark clusters with the Photon execution engine
  • Unity Catalog for governance and lineage
  • Delta Lake open table format
  • Notebook-based collaborative workspace
  • MLflow for experiment tracking and model deployment
  • Databricks SQL for warehouse-style BI workloads

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Apache Spark Open-source distributed processing engine for large-scale batch, SQL, streaming and machine-learning workloads. Starburst Commercial data platform built on Trino, offering managed and self-hosted federated SQL query with enterprise governance. Trino Open-source distributed SQL engine that queries data across warehouses, lakes and databases without moving it.

Databricks in the index now

Terms to know

Data lakehouse A storage architecture that adds warehouse-style structure and transactions directly on top of low-cost data lake storage. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Apache Spark An open-source distributed computing engine for processing large datasets in parallel across a cluster of machines. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Open table format A specification that adds transactions, schema management, and versioning to files stored in a data lake.

Related guides

How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a lakehouse platform Choosing a lakehouse means two decisions, not one — which open table format your data sits in, and which platform manages and queries it. How to reduce data warehouse costs Where usage-based warehouse spend actually goes, and the levers — query, storage, scheduling — that reliably bring it down.