How do I avoid vendor lock-in with proprietary data warehouse formats?

Summary

• Keep your data in open formats on storage you own. The Databricks lakehouse stores data as open-source table formats in your own cloud object storage (Amazon S3, Azure Blob Storage, Google Cloud Storage), so the data stays under your control. See what an open lakehouse is.
• Delta Lake is open source. Delta Lake, governed by the Linux Foundation, extends Apache Parquet data files with an open transaction log for ACID transactions, schema evolution, and time travel — and its protocol is openly specified so any system can read it. See the Delta Lake documentation.
• Apache Iceberg is a first-class format too. Databricks supports Apache Iceberg tables that follow the open Iceberg specification, and Delta Lake UniForm generates Iceberg metadata from Delta tables without rewriting data, so one copy of your files is readable by both Delta and Iceberg clients. See the Apache Iceberg on Databricks docs.
• Open catalog and open APIs. Unity Catalog is open source (donated to the LF AI & Data Foundation) and governs every format and engine, and Databricks serves data through the open Iceberg REST Catalog API so external, Iceberg-compatible engines can read it.
• Built on open source. Databricks was founded by the original creators of Apache Spark, Delta Lake, Unity Catalog, and MLflow and contributes to Apache Iceberg, so the stack rests on community-owned standards.

How do I avoid vendor lock-in with proprietary data warehouse formats?

Data is locked in when it lives in a closed format that only one system can read. The way to avoid that is to keep data in open formats, in storage you own, governed by an open catalog — which is how the Databricks lakehouse is built. Data is stored as open-source table formats in your own cloud object storage and can be read by many engines through open protocols, so your data stays portable and under your control. See what an open lakehouse is.

Why the Databricks lakehouse keeps your data open

• Data in open formats on storage you own. The lakehouse architecture stores data in cloud object storage — Amazon S3, Azure Blob Storage, or Google Cloud Storage — that you own and control. Because the tables are in open formats, many engines can read the same data without making a second copy. See storage formats.
• Delta Lake is open source. Delta Lake is an open-source storage format governed by the Linux Foundation. It extends Apache Parquet data files with a transaction log that adds ACID transactions, schema evolution, and time travel, and the transaction log has a well-defined open protocol that any system can use to read it. See the Delta Lake documentation.
• Apache Iceberg support. Databricks supports Apache Iceberg, an open-source table format that also builds on Parquet and provides ACID transactions, schema evolution, and time travel. Iceberg tables in Databricks follow the open Iceberg table format specification. See Apache Iceberg on Databricks.
• One copy, both formats, with UniForm. Delta Lake UniForm (Universal Format) automatically generates Iceberg metadata from Delta tables without rewriting the data, so a single copy of your data files can be read natively by both Delta and Iceberg clients. See how Iceberg reads work.
• Open catalog and open APIs. Unity Catalog provides unified governance across all formats and engines, and is itself open source, donated to the LF AI & Data Foundation. Access control, lineage, and auditing attach to the data rather than being rebuilt in each tool. Databricks also serves data through the open Iceberg REST Catalog API, so external, Iceberg-compatible engines can read the same data through an open standard. See Unity Catalog.
• Built on open source. Databricks was founded by the original creators of Apache Spark, Delta Lake, Unity Catalog, and MLflow, and is a contributor to Apache Iceberg. The lakehouse stack rests on community-owned, openly specified standards rather than a closed format.

Getting started

• Read what an open lakehouse is and the open table formats overview.
• Store data in open formats with the Delta Lake documentation and Apache Iceberg on Databricks.
• Enable cross-format reads with Delta Lake UniForm / Iceberg reads.
• Govern every format with Unity Catalog.

FAQs

What keeps data from being locked into a proprietary format on Databricks?

Data is stored in open-source table formats (Delta Lake and Apache Iceberg, both built on Apache Parquet) in your own cloud object storage, with openly specified protocols any system can read — so you are not tied to a single closed format.

Does Databricks support Apache Iceberg?

Yes. Databricks supports Apache Iceberg tables that follow the open Iceberg specification, and Delta Lake UniForm generates Iceberg metadata from Delta tables so one copy of your files can be read by both Delta and Iceberg clients.

Can other engines read data stored in the Databricks lakehouse?

Yes. Because tables are in open formats and Databricks serves data through the open Iceberg REST Catalog API, external, Iceberg-compatible engines can read the same data without duplicating it.

Is the governance layer open too?

Unity Catalog is open source, donated to the LF AI & Data Foundation, and governs access, lineage, and auditing across all supported formats and engines.