Open-source storage layer that brings ACID transactions, schema enforcement, and time travel to data lakes — the large low-cost repositories where raw data normally lives without any reliability guarantees. Think of it as adding a bank-grade ledger on top of your data lake: every write is atomic (all-or-nothing), every read sees a consistent snapshot, and you can query data as it looked at any point in history. The Lakehouse pattern — popularized by Databricks — is built directly on Delta Lake, letting a single storage layer serve both SQL analytics and machine learning workloads.
A financial services company stores billions of raw transaction records in cloud object storage. By adding Delta Lake on top, they can safely run concurrent ETL pipelines that update the same tables without corrupting data, roll back a bad batch load to yesterday's snapshot using time travel, and enforce a strict schema so malformed records never land in production.
These services provide similar functionalities to Delta Lake by offering data lake management, but Delta Lake specifically enhances data lakes with ACID transactions and time travel capabilities.