What are the best open lakehouse format solutions?

Summary

• Open lakehouse architectures layer open table formats like Delta Lake, Apache Iceberg, and Apache Hudi on cloud object storage to combine low-cost storage with warehouse-grade analytics and eliminate vendor lock-in.
• Databricks delivers an open lakehouse with first-class support for Delta Lake, Iceberg, and Parquet, unified governance through Unity Catalog, and AI-powered performance optimizations like Photon and Predictive IO.
• Organizations should migrate incrementally by identifying high-value workloads first, unifying governance early with a single catalog, and validating query performance before decommissioning legacy systems.

Open lakehouse format solutions: how to unify your data without lock-in

Organizations running analytics across separate data lakes and warehouses face a familiar problem. Data gets duplicated, governance fragments across tools, and proprietary formats create vendor lock-in. Open lakehouse formats offer a way out, combining low-cost lake storage with structured query performance, built entirely on open standards.
According to a 2024 MIT Technology Review Insights survey, 77% of organizations report significant challenges integrating data across multiple platforms and formats (MIT Technology Review Insights, "The Great Data Integration Challenge," 2024).

What makes an open lakehouse architecture different?

Traditional architectures force a choice: cheap storage with a data lake or fast analytics with a data warehouse. Open lakehouse architectures remove that trade-off by layering open table formats on top of cloud object storage.
Core layers include:

• Cloud object storage, S3, ADLS, or GCS holding data in open file formats like Parquet
• Open table format, Delta Lake, Apache Iceberg™, or Apache Hudi providing ACID transactions, schema evolution, and metadata management
• Unified metadata catalog, governing access, lineage, and business definitions across all data assets
• Decoupled compute engines, separate processing for SQL analytics, ETL, and AI/ML workloads

No single vendor controls the data in this model. Open table formats ensure interoperability across engines. Teams choose the best tool for each workload without duplicating data.

Key open table formats compared

Capability	Delta Lake	Apache Iceberg	Apache Hudi
ACID transactions	Yes, transaction log on Parquet	Yes, snapshot-based metadata	Yes, timeline-based
Schema evolution	Add, rename, reorder columns	Full schema evolution support	Schema-on-read and schema-on-write
Time travel	Yes	Yes	Yes
Engine compatibility	Spark, Flink, Trino, others	Spark, Flink, Trino, Presto, others	Spark, Flink, Presto
Governance integration	Unity Catalog, other catalogs	REST catalog, Hive Metastore, others	Hive Metastore

Each format takes a different approach to metadata management and file organization. The right choice depends on your existing ecosystem, engine preferences, and governance requirements.

How open formats enable interoperability

Open table formats store data in vendor-neutral files with standardized metadata layers. Any compatible engine reads and writes the same tables without format conversion or data movement.
This matters because teams across an organization use different tools. Data engineers may prefer Spark. Analysts may use Trino or a SQL warehouse. ML engineers may need direct file access.
Open formats let all of them work from one copy of the data.

How the Databricks Data + AI Platform delivers an open lakehouse

Databricks provides warehouse-grade performance on an open lakehouse foundation. Delta Lake, Apache Iceberg, and Parquet are first-class citizens, not bolt-ons. This maintains a single trusted source for every tool.
Unity Catalog provides one catalog for all data, managing Delta Lake, Apache Iceberg, and Parquet with a single set of permissions, lineage, and business definitions. Governance, semantics, and performance are built directly into the platform.
AI-powered optimizations, including Photon, Predictive IO, and Intelligent Workload Management, deliver speed and concurrency without the trade-offs of proprietary warehouses.

Why open formats are going mainstream

Major cloud platforms now support open table formats in various capacities. Snowflake, Google BigQuery, Amazon Redshift, and Microsoft Fabric each offer some level of Iceberg or open format support. This signals a broad industry shift toward lakehouse-style architectures.
For organizations evaluating their options, this broader adoption reduces risk. Building on open formats no longer means betting on a single vendor's roadmap.

Best practices for migrating to an open lakehouse

1. Identify high-value workloads, start with tables that suffer the most duplication or lock-in
2. Choose your table format, evaluate Delta Lake, Iceberg, and Hudi against your engine and tooling needs
3. Migrate incrementally, convert tables in phases rather than all at once
4. Unify governance early, establish a single catalog and permission model before scaling
5. Validate performance, benchmark query speed and concurrency before decommissioning legacy systems

For detailed guidance on moving from legacy systems, see warehouse lakehouse migration approaches.

FAQs

What is an open lakehouse architecture and how does it work?

An open lakehouse stores data in open formats on cloud object storage and layers table formats on top for ACID transactions and metadata. Any compatible engine can read the same data without duplication.

What are the key components of open table formats such as Delta Lake, Apache Iceberg, and Apache Hudi?

They add ACID transactions, schema evolution, time travel, and metadata management on top of Parquet files. They sit between raw storage and compute engines, enabling warehouse-like reliability on lake storage.

How does the open table format enable interoperability across different data processing engines?

They store data in vendor-neutral files with standardized metadata. Any compatible engine reads and writes the same tables without format conversion.

What are the benefits of using open lakehouse formats for enterprise data management?

Benefits include eliminating vendor lock-in, reducing data duplication, unifying governance, and lowering storage costs. Databricks extends these with Unity Catalog for centralized permissions, lineage, and business definitions.

How do you implement an open lakehouse architecture on cloud infrastructure?

Store data in open formats on cloud object storage. Add a unified catalog and governance layer. Then connect compute engines for SQL, ETL, and ML workloads.

What role does Apache Iceberg play in building an open lakehouse solution?

Iceberg is an open table format that maintains data integrity, supports schema evolution, and optimizes query performance through snapshot-based metadata tracking.

Build your open lakehouse on a trusted foundation

Open lakehouse formats give organizations the flexibility to avoid lock-in while unifying analytics, governance, and AI on a single copy of data. Databricks combines the openness of the lakehouse with warehouse-grade performance, unified governance through Unity Catalog, and first-class support for Delta Lake, Apache Iceberg, and Parquet, delivering consistent, trusted insights at scale.
Ready to explore further? Start with the Databricks Data + AI Platform to see how open formats, unified governance, and AI-powered performance work together.