Compare
Avro vs Parquet
Avro serializes schema-governed records; Parquet stores data by column for large-scale analytical reads. Choose by how the data is written and queried.
Which format should you choose?
Consider Avro for event and record exchange, and Parquet for column-oriented analysis and aggregation.
Good fit for Avro
Exchange records with a defined schema
- Events and data pipelines that track schema versions
Good fit for Parquet
Store structured tables for analytics
- Large datasets read by selected columns
Basic information
| Field | Avro | Parquet |
|---|---|---|
| Extension | .avro | .parquet |
| MIME type | application/avro | application/vnd.apache.parquet |
| Common uses | Exchange records with a defined schema | Store structured tables for analytics |
| How to open | Use an Avro-aware tool to inspect the schema, fields, types, and records. | Use a Parquet-aware tool to inspect schema, column types, row counts, and values. |
| Export or convert | When converting to JSON or Parquet, verify field names, types, nulls, logical types, record counts, and schema compatibility. | From CSV or other formats, check types, nulls, timestamps, encoding, partitions, and row counts. |
Choose by use case
Events and data pipelines that track schema versions
Exchange records with a defined schema
AvroLarge datasets read by selected columns
Store structured tables for analytics
ParquetKey point 1
Avro centers on records and schemas.
More on the differencesKey point 2
Parquet organizes data by column for analytics.
More on the differencesMore on the differences
Check schema evolution, engine support, and representative read patterns before standardizing. Test actual files and queries.
- Avro is suited to record-oriented serialization.
- Parquet is designed for analytical column reads.
- Schema changes and query characteristics affect the choice.
Frequently asked questions
Can one format replace the other everywhere?
Not generally. Event exchange and analytical scans have different access patterns and tool requirements.