Databricks review 2026: lakehouse architecture, Unity Catalog, MLflow, and DBU-based pricing explained, plus how it stacks up against Snowflake and BigQuery.
Databricks is a unified Data Intelligence Platform built around the lakehouse architecture, a design that merges the scalability and low storage cost of a data lake with the governance and performance guarantees of a traditional data warehouse. It was founded in 2013 by the team behind Apache Spark and today runs on AWS, Azure, and Google Cloud.
Rather than forcing teams to stitch together separate tools for data engineering, analytics, and machine learning, Databricks lets everyone work against the same governed copy of data through notebooks, SQL editors, and increasingly AI-assisted interfaces.
Core capabilities include Delta Lake for ACID-compliant storage on cloud object storage, Unity Catalog for centralized governance and lineage across data and AI assets, Databricks SQL for warehouse-style analytics, and MLflow for machine learning lifecycle management.
The Mosaic AI suite adds generative AI tooling, including model fine-tuning, retrieval-augmented generation pipelines, and an in-product AI assistant, positioning Databricks as a platform for building custom enterprise AI applications on top of proprietary data.
Databricks bills on consumption using Databricks Units (DBUs), a normalized measure of compute power charged per second, on top of the underlying cloud infrastructure costs from AWS, Azure, or GCP.
The Premium tier is now the standard entry point following the retirement of the older Standard tier, while Enterprise adds compliance, security, and support features at a negotiated premium. There is no fixed monthly price; a free trial with usage credits is available for new users.
Databricks is used for large-scale data engineering, data warehousing, business analytics, and machine learning or generative AI development on a single unified lakehouse platform.
Databricks is not free for production use, but it offers a free trial with usage credits. Ongoing use is billed on a pay-as-you-go basis via Databricks Units plus underlying cloud compute costs.
A DBU is Databricks' normalized unit of processing capability, billed per second based on the compute product used, such as all-purpose compute, jobs compute, or serverless SQL.
Databricks is built around an open lakehouse architecture with strong roots in open-source big data and machine learning tooling, while Snowflake is primarily a SQL-first cloud data warehouse; both have expanded to cover overlapping use cases.
Most Databricks workflows use SQL, Python, Scala, or R in notebooks, so it generally requires coding skills, though Databricks SQL offers a more analyst-friendly, warehouse-style interface.
Databricks is available on Amazon Web Services, Microsoft Azure, and Google Cloud Platform.