Databricks is the company founded by the original creators of Apache Spark, and its platform is the leading expression of the lakehouse - an architecture that puts warehouse-grade reliability and performance directly on open data-lake storage. Instead of moving data between a lake for raw storage and a separate warehouse for analytics, Databricks unifies engineering, analytics, and machine learning over one copy of the data. For organizations that do heavy data processing and ML alongside SQL analytics, that consolidation is the draw.
The lakehouse foundation
Two technologies anchor the platform. Apache Spark provides distributed processing for large-scale transformation and ML. Delta Lake - an open table format - adds ACID transactions, schema enforcement, and time travel to files in cloud storage, so a data lake gains the reliability that previously required a warehouse. On top of these, Unity Catalog governs access, lineage, and discovery across the whole estate, and Databricks SQL serves warehouse-style queries and BI connectivity over the same data.
One platform, many workloads
Databricks runs collaborative notebooks in Python, SQL, Scala, and R; orchestrates jobs and pipelines; and integrates MLflow for experiment tracking and model management. The pitch is that data engineers building pipelines, analysts running SQL, and data scientists training models all work against the same governed data without copying it into separate systems - reducing the integration tax that fragmented stacks impose.
Where it fits
- Organizations combining large-scale data engineering, SQL analytics, and machine learning.
- Teams that want open formats (Delta Lake, increasingly Iceberg) rather than a proprietary warehouse.
- Spark-based processing at scale with governance via Unity Catalog.
When to look elsewhere
For pure SQL analytics with minimal engineering overhead, a classic warehouse like Snowflake or BigQuery can be simpler to adopt and operate. If your need is real-time, sub-second analytics on event streams, ClickHouse is purpose-built for that. Databricks rewards teams that genuinely use its breadth; for narrow SQL use, it can be more platform than required.
How it compares
The long-running comparison is Databricks versus Snowflake: Databricks comes from data engineering and ML on open lakehouse storage, Snowflake from SQL-first warehousing, and the two have been converging from opposite ends. Databricks' edge is unified Spark-scale processing and ML; Snowflake's is SQL simplicity and ecosystem. To work in its engine directly, the PySpark course and Spark Internals course cover the foundations Databricks runs on.
Frequently asked questions
What is the Databricks lakehouse? An architecture that combines a data lake's open, cheap storage with a warehouse's reliability and performance, using Delta Lake and Unity Catalog over cloud storage.
What is Delta Lake? An open table format that brings ACID transactions, schema enforcement, and time travel to files in a data lake - the storage layer beneath the Databricks lakehouse.
Databricks vs Snowflake - what's the difference? Databricks centers on Spark-based engineering and ML over open lakehouse storage; Snowflake centers on SQL-first cloud warehousing. They increasingly overlap.