Apache XTable: A Universal Translator for Data Lake Format Interoperability

1. Design Motivation: Why XTable?

The modern data lakehouse ecosystem revolves around three dominant table formats—Apache Iceberg, Apache Hudi, and Delta Lake—each with strong capabilities and large ecosystems. However, these formats are isolated by thick “walls”: choosing one often locks you into a specific compute engine or forces costly ETL processes to migrate data.

Vendor lock‑in : Databricks favors Delta Lake, Snowflake embraces Iceberg, EMR/Athena may better support Hudi or Iceberg, leading to multiple data copies.

High migration cost : Moving from Hudi to Iceberg typically requires rewriting all historical data.

Architectural rigidity : A format selected early in a project may become unsuitable as business evolves, yet changing it is prohibitively expensive.

The core purpose of Apache XTable (formerly OneTable) is to break these walls by decoupling the "data itself" from the "metadata format". Users can pick the most suitable write format while any read engine can access the data without duplicating files.

2. Core Principle: How It Works

Apache XTable is not a new storage engine; it is a lightweight metadata translation layer . All three lake formats store data in standard Parquet files, differing mainly in how they organize and record metadata (file lists, snapshots, schema evolution, etc.). XTable introduces a zero‑copy approach that translates metadata between formats.

The workflow consists of three steps:

Read source metadata : Parse the commit log and file manifest of the source format (e.g., Hudi).

Translate to an intermediate state : Map the source metadata to XTable’s internal representation.

Write target metadata : Emit the internal representation as the metadata files required by the target format, such as metadata.json for Iceberg or _delta_log for Delta Lake.

After translation, the same set of Parquet files in an S3 bucket is accompanied by valid Iceberg and Delta metadata, enabling all formats to point to the identical data.

3. What Apache XTable Can Do

3.1 Omni‑directional Interoperability

XTable supports multi‑directional conversion:

Hudi → Iceberg & Delta

Delta → Hudi & Iceberg

Iceberg → Hudi & Delta

This lets you write using Hudi’s upsert/compaction capabilities, read with Snowflake’s native Iceberg support, and run machine‑learning training on Databricks‑optimized Delta tables—all on the same underlying Parquet data.

3.2 Gradual Migration

Instead of a one‑time, “big‑bang” rewrite, XTable allows you to maintain views in multiple formats simultaneously. Teams can safely test a new format, verify performance and compatibility, and transition smoothly with minimal risk.

3.3 Unified Data View

In large organizations where different departments use different stacks (e.g., Databricks/Delta vs. Flink/Hudi), XTable acts as middleware that virtualizes heterogeneous sources into a single catalog‑friendly format, eliminating the need for physical data movement.

4. Real‑World Scenario

Assume you are a real‑time data‑warehouse architect:

Write side : Use Flink with Apache Hudi to ingest streaming logs, leveraging Hudi’s strong streaming write and primary‑key indexing.

Conversion side : Deploy a lightweight XTable job that scans the Hudi table every five minutes and generates corresponding Iceberg metadata.

Consume side :

Data analysts query the Iceberg view with Snowflake or Amazon Athena, enjoying high‑performance queries.

Data scientists read the Hudi format with Spark for feature engineering.

During this process no Parquet files are copied, storage cost remains essentially unchanged, and data latency stays extremely low.

5. Conclusion

Apache XTable redefines openness in data lakes. By inserting an abstraction layer, it turns data‑format choice into an optional, even co‑existent, attribute rather than an exclusive decision, achieving a true "Write Once, Read Anywhere" capability.