Choosing the right file format and storage strategy is critical for building scalable, cost-effective, and high-performance data lakes. Two of the most commonly used technologies in this space are Apache Parquet and Apache Hudi.

While Parquet is a columnar file format optimized for analytical workloads, Hudi is a data lake platform that supports versioning, upserts, and transactional capabilities on top of data formats like Parquet and ORC.

This article compares Hudi vs. Parquet in depth, helping you decide which is best for your data lake architecture.

What is Apache Parquet?

Apache Parquet is an open-source, columnar file format designed for efficient data storage and processing. It is widely used in Hadoop ecosystems and supported by engines like Spark, Hive, Presto, and Athena.

Key features:

  • Columnar storage for high compression and fast scans
  • Optimized for read-heavy workloads
  • Schema evolution support
  • Interoperable with multiple tools and languages

Parquet is storage only — it does not manage metadata, versioning, or transactions.

What is Apache Hudi?

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is a data lake framework built to manage large-scale, mutable datasets. Hudi brings transactional semantics to data lakes by managing file versions, metadata, and commit timelines.

Key features:

  • ACID-compliant upserts, inserts, and deletes
  • Copy-on-Write (COW) and Merge-on-Read (MOR) storage options
  • Incremental data ingestion and time-travel queries
  • Hive and Glue metastore integration
  • Built-in compaction and clustering

Hudi uses Parquet as the underlying file format for its base files.

Hudi vs. Parquet: Feature Comparison

Feature Parquet Hudi
Format Type File format Data management layer
ACID Transactions ❌ ✅
Upsert Support ❌ (requires overwrite) ✅
Time Travel ❌ ✅
Metadata Table ❌ ✅
Schema Evolution ✅ ✅
Incremental Reads ❌ ✅
Write Performance ✅ (simple writes) ⚠️ (depends on upserts, compaction)
Query Performance ✅ ✅ (depends on table type)
Compaction Needed ❌ ✅ (MOR only)
Supported by Spark, Hive, Presto Spark, Hive, Flink, Presto, Athena

When to Use Apache Parquet

Choose Parquet when:

  • Your data is append-only
  • You don’t need updates or deletes
  • You want simple storage and fast querying
  • You use Presto, Athena, or Redshift with no mutation requirements
  • You don’t need to manage data lifecycle or versioning

Example use cases:

  • Analytics on event logs
  • Read-only datasets
  • Periodic batch inserts

When to Use Apache Hudi

Choose Hudi when:

  • You need frequent updates, deletes, or upserts
  • You’re building real-time or streaming data pipelines
  • You want to support time-travel queries
  • You need to process incremental data
  • You want ACID guarantees over cloud object stores like S3

Example use cases:

  • Change Data Capture (CDC) pipelines
  • Real-time user tracking
  • ETL for mutable datasets (e.g., inventory, orders)

Performance Considerations

  • Parquet is simpler and faster for pure reads, especially when data is immutable.
  • Hudi introduces some overhead for metadata management, compaction (in MOR), and commit timelines — but enables richer capabilities.
  • Choose Copy-on-Write tables in Hudi for better read performance.
  • Use Merge-on-Read for high-throughput writes with delayed compaction.

Coexistence Strategy

You can use both Hudi and Parquet in the same architecture:

  • Use Parquet for raw/immutable layers (Bronze)
  • Use Hudi for refined, enriched, or mutable datasets (Silver/Gold)
  • This supports both read-optimized queries and mutation-intensive pipelines

Conclusion

Apache Hudi and Parquet serve different but complementary purposes in a modern data lake. While Parquet is ideal for read-heavy, static data, Hudi excels in managing mutable, real-time datasets with transactional integrity.

Use Parquet when simplicity and speed matter most, and choose Hudi when you need advanced capabilities like upserts, incremental reads, and streaming ingestion — especially for building reliable, ACID-compliant data lakehouses.