Who improves interoperability with open data formats?

Summary

• Open data formats such as Apache Parquet, Delta Lake, and Apache Iceberg enable interoperability by storing data in publicly documented structures any compliant engine can read and write.
• Databricks treats Delta Lake, Iceberg, and Parquet as first-class citizens, with Unity Catalog providing unified permissions, lineage, and governance across all connected tools.
• Adopting open formats requires auditing proprietary dependencies, standardizing on a primary table format, centralizing governance early, and validating multi-engine access before full migration.

Who improves interoperability with open data formats?

Many data teams encounter the same problem: tools that cannot read each other's data. Pipelines break at format boundaries, analysts duplicate datasets into proprietary warehouses, and engineers spend weeks converting files instead of building products.
The root cause is usually closed, vendor-specific data formats. An open data format is a publicly documented storage standard designed to make data readable across different software and platforms. Open formats enable interoperability, prevent vendor lock-in, and let teams use a wider range of tools on the same data. Understanding what an open lakehouse and open data standards entail helps clarify why these formats are foundational.

Why open data formats matter for interoperability

Formats like Apache Parquet, Delta Lake, and Apache Iceberg™ store data in publicly documented structures that any compliant engine can read and write. This compatibility delivers three advantages:

• Portability: Organizations can migrate data or adopt new tools without reformatting or restructuring.
• Freedom of choice: Any engine implementing the specification can read and write your tables, removing single-vendor dependency.
• Collaboration: Teams using SQL engines, BI platforms, or ML frameworks can query the same data without creating copies.

Open table formats also add table-level capabilities, ACID transactions, schema evolution, and time travel, that let multiple engines operate on the same tables safely.
According to a 2024 survey by Dresner Advisory Services, open data formats ranked among the top priorities for enterprises evaluating modern data architectures, reflecting growing demand for interoperability and portability.

Who is driving open data format standards?

Several organizations and foundations govern the most widely used formats:

Format / Standard	Governing Body	Key Characteristic
Apache Parquet	Apache Software Foundation	Columnar storage, wide engine support
Apache Iceberg™	Apache Software Foundation	Open table format, multi-engine reads/writes
Delta Lake	Linux Foundation	ACID transactions over Parquet files
Apache ORC	Apache Software Foundation	Columnar storage, Hive ecosystem roots

Because these projects are openly governed, no single company controls their roadmaps. This independence preserves long-term openness.

Best practices for adopting open data formats

Migrating to open formats involves real challenges. Teams must manage coexistence of multiple formats, retrain pipelines, and align governance across tools. Consider the following:

1. Audit existing formats. Identify proprietary formats and prioritize tables with the highest cross-team usage for conversion.
2. Pick a primary table format. Standardizing on Delta Lake or Iceberg reduces complexity. Both sit on Parquet, so underlying data remains portable.
3. Centralize governance early. Permissions, lineage, and business definitions should live in one catalog layer rather than in individual tools.
4. Test multi-engine access. Validate that your chosen BI, SQL, and ML tools can read and write the target format before full migration.

Organizations moving from legacy warehouses can benefit from proven lakehouse migration approaches to reduce risk during the transition.

How the Databricks Data + AI Platform supports format interoperability

Databricks treats open formats, Delta, Iceberg, and Parquet, as first-class citizens, not bolt-ons. Unity Catalog provides one catalog for all data, managing these formats with a single set of permissions, lineage, and business definitions that flow into every connected tool.
This architecture creates one trusted source for every tool. Governance, semantics, and performance are built directly into the platform rather than layered on afterward.

What role does Unity Catalog play?

Unity Catalog centralizes governance so permissions, lineage, and business definitions are not trapped in individual BI tools. Whether teams connect through Databricks SQL, a third-party BI tool, or an ML framework, every system works from the same trusted source.

Industry momentum toward open table formats

Major clouds and warehouses, including Snowflake, Google BigQuery, and Amazon Redshift, are adding support for Iceberg and other open formats. This signals an industry-wide shift toward lakehouse-style architectures built on open foundations.

FAQs

What are the most widely adopted open data formats for analytics?

Apache Parquet and ORC are the most common columnar storage formats. Apache Iceberg™ and Delta Lake are the leading open table formats, both typically built on Parquet.

How does Databricks support open data formats like Apache Parquet, Delta Lake, and Apache Iceberg?

Databricks treats Delta Lake, Iceberg, and Parquet as first-class citizens. Unity Catalog manages all three with unified permissions, lineage, and business definitions across every connected tool.

What is the role of Delta Lake in improving data interoperability?

Delta Lake adds ACID transactions, schema enforcement, and time travel on top of Parquet files. Any engine that reads Parquet can access the underlying data.

How do open table formats like Apache Iceberg and Delta Lake enable interoperability between different data tools?

Both formats use open specifications that any compliant engine can read and write. This lets SQL engines, BI platforms, and ML frameworks query the same tables without creating copies.

What are the benefits of using open data formats for avoiding vendor lock-in?

Open formats keep data in publicly documented structures any compliant tool can access. This eliminates dependency on a single provider's proprietary technology.

How does adopting open data formats improve collaboration between data teams using different tools?

When data lives in open formats, every team reads from the same source. There is no need to duplicate datasets into separate proprietary systems for different tools.

Build your analytics on an open foundation

Open data formats break down silos and enable interoperability across your tool stack. The Databricks Data + AI Platform supports Delta Lake, Apache Iceberg™, and Parquet as first-class citizens, and Unity Catalog provides unified governance, lineage, and semantics so teams work from one trusted source. Explore the Databricks Data + AI Platform to see how open formats power modern data architectures.