open lakehouse format solutions

Summary

• Open lakehouse formats are metadata layers that add ACID transactions, schema evolution, and time travel to data stored on object storage, giving you data warehouse reliability without moving data.
• Delta Lake, Apache Iceberg, and Apache Hudi are the three leading formats, each optimized for different workload patterns.
• Interoperability is improving: Delta UniForm lets Iceberg clients read Delta tables, and Iceberg v3 introduces compatible implementations with Delta and Apache Parquet.
• Unified governance across formats through Unity Catalog eliminates the need for separate governance models.

open lakehouse format solutions
Lakehouse formats are metadata layers (Delta Lake, Apache Iceberg, Apache Hudi) built on object storage that transform raw files into reliable, transactional tables. The goal is simple: give you the cost and openness of a data lake with the reliability of a data warehouse. All three solve the same core problem: ACID transactions, schema evolution, and time travel over data in place.

What makes an open format

An open lakehouse format is defined by its specification and vendor independence. Delta Lake has an open protocol for its transaction log usable by any system. Apache Iceberg publishes an open table format specification allowing any engine to read and write tables. Both can be implemented without licensing or proprietary agreements, so you avoid lock-in and can migrate compute without moving data. Unity Catalog provides governance across both formats without data duplication.

Key formats and their trade-offs

Format	Strengths	Best For
Delta Lake	Widely adopted, mature, strong Spark integration	Batch and streaming on Databricks and Spark
Apache Iceberg	Schema evolution, hidden partitioning, metadata pruning	Complex analytics, multi-engine scenarios
Apache Hudi	Record-level upserts, streaming optimized	Continuous ingestion, streaming architectures

Delta Lake emerged from Databricks' batch processing needs; Iceberg was designed for multi-engine and warehouse scenarios; Hudi specializes in streaming and record-level mutations.

Interoperability and convergence

Recent innovations bridge these formats:
Delta UniForm: Automatically generates Iceberg metadata from Delta tables without rewriting data, allowing Iceberg-compatible engines to read Delta tables natively, eliminating data duplication.
Iceberg v3 unification: Introduces compatible implementations across Delta, Parquet, and Spark. Deletion vectors use the same encodings. Row-level lineage in Iceberg v3 is compatible with Delta Lake tracking.
Catalog-coordinated commits: Delta Lake and Iceberg share a single governance model through catalog-coordinated transactions.
Iceberg REST APIs: External analytics engines can query Unity Catalog-registered tables without duplicate copies or format conversion.

How Databricks approaches this

Databricks supports Delta Lake as its primary format and Iceberg for multi-engine scenarios. Both are governed by Unity Catalog under one permission and audit model. Delta UniForm lets external Iceberg clients read Delta tables directly. For mixed-engine organizations, start with the format matching your primary engine stack, then confirm your catalog can accommodate a second format later.
Governance, lineage, and access control apply uniformly across both formats through Unity Catalog.

Use cases

Unified batch and streaming: Ingest batch data with Delta Lake and stream real-time events through Hudi, both governed by one access control model.
Multi-warehouse federation: Use Delta tables as the primary store and expose subsets to external analytics platforms via Iceberg REST endpoints and UniForm, so teams query without moving data.
Schema evolution: Evolve table schema as features change. Both Delta and Iceberg preserve schema metadata and time travel, so historical queries continue to work.
Multi-format optimization: Use Iceberg for analytics, Hudi for streaming upserts, Delta for batch, all in one catalog.

FAQs

Should I pick Delta Lake or Iceberg?

Start with the format matching your engine stack. Delta Lake is native to Spark and Databricks. Iceberg is best for multi-engine or complex analytics scenarios. Convergence features like Delta UniForm make the choice less critical.

Can I query both formats in one query?

Yes. Both are registered in Unity Catalog and queryable from the same SQL context. You can join Delta and Iceberg tables in a single query.

Do I need separate governance for each format?

No. Unity Catalog applies one access control and lineage model across both formats, regardless of the underlying format.

Can external engines read my Delta tables?

Yes, through Delta UniForm and Iceberg REST endpoints. UniForm generates Iceberg metadata, and external Iceberg clients can read Delta tables without data duplication.