Databricks Variant: The Open Standard For Semi-Structured Data in the Lakehouse

Organizations increasingly face the challenge of managing vast amounts of semi-structured data, such as logs and telemetry, in analytics and AI workflows. Historically, teams had to choose between slow, flexible string storage or fast, rigid structs. Variant is a newly ratified open standard in Apache Parquet™ that eliminates this compromise to enable both flexibility and high performance for data lakehouses.

Solving the Flexibility-Performance Dilemma

The traditional approaches to semi-structured data management have forced difficult tradeoffs. Storing data as strings offers schema flexibility but demands costly parsing at query time, leading to latency. 

Converting data to structs accelerates queries but locks teams into fixed schemas. Variant bridges the gap through a compact, binary-encoded format that is engine-agnostic, delivering fast queries without sacrificing adaptability.

How Variant Delivers Efficiency

With Variant, both data values and schema definitions are stored together in a binary format. This design enables analytic engines to jump directly to needed fields using internal offsets, bypassing the need to scan or parse entire JSON blobs. As a result, queries can access specific attributes swiftly, unlocking significant speed improvements for data teams.

Unlocking Further Gains with Shredding

Shredding takes Variant’s performance to the next level by extracting frequently accessed fields and storing them as distinct columns within the Parquet file. This approach yields multiple benefits:

• Pruned I/O: Only necessary fields are read, improving efficiency.
  
  
• Data skipping: Columnar storage allows analytic engines to skip irrelevant data blocks, expediting queries.
  
  
• Improved compression: Typed, column-based data compresses better, reducing storage costs.

Benchmarks reveal that Variant enables up to 8x faster reads versus string storage, and shredding can boost this to 30x, all while retaining schema flexibility for evolving data.

An Open and Interoperable Ecosystem

Variant’s adoption is the result of collaborative work by Databricks and the open source community. The standard is now natively supported in Apache Parquet (v2.12.0+ and Parquet Java v1.16.0+), with major lakehouse engines like Delta Lake and Apache Iceberg™ integrating Variant columns. Official specifications for binary encoding and shredding are publicly available, ensuring robust interoperability across Spark, Arrow, Delta Lake, and Iceberg environments.

Seamless Integration with Existing Workflows

Adopting Variant is straightforward for organizations already using modern data lakehouses. Databricks supports Variant columns and shredding in DBR 17.2+ and DBSQL 2025.30+, allowing teams to ingest data from formats like JSON, XML, and CSV. The transition requires minimal code changes, so users can immediately benefit from higher performance and flexibility without refactoring ETL pipelines.

Empowering Next-Gen Analytics

Variant marks a new era for semi-structured data management, combining the openness of a shared standard with the performance needed for big data analytics. Its binary format and shredding capability empower organizations to analyze complex, ever-evolving data reliably and cost-effectively. As the volume and diversity of data continue to grow, Variant lays the foundation for scalable, future-ready analytics in the lakehouse ecosystem.

Source: Databricks Blog