Apache Arrow vs Parquet
Apache Arrow and Apache Parquet are both columnar data formats developed in the same ecosystem, but they serve different purposes in a data pipeline. Arrow is designed for speed of processing in memory; Parquet is designed for efficiency of storage on disk. Understanding the difference determines whether you should persist data as Arrow IPC files, Parquet files, or convert between them.
What is Arrow?
Apache Arrow defines a language-independent columnar memory format and an IPC (Inter-Process Communication) file format for persisting Arrow data to disk. The format is designed for zero-copy reads — data can be memory-mapped and processed directly without deserialization overhead. DuckDB, pandas, Polars, PySpark, and Ray all use Arrow as their internal in-memory representation.
Arrow IPC files preserve the exact in-memory layout of an Arrow table. This means reading an Arrow file requires no decompression or deserialization — the data is ready to process immediately. Arrow is the right choice when speed of access matters more than storage efficiency.
What is Parquet?
Apache Parquet is an open-source binary columnar storage format designed for efficient disk storage and query performance. It applies compression codecs (Snappy, Zstandard, Gzip) and encoding schemes (dictionary encoding, delta encoding) that typically reduce file size to 10–30% of equivalent CSV. The schema is embedded in the file footer.
Parquet is the native storage format of the modern data stack: AWS Athena, Google BigQuery, Apache Spark, Delta Lake, Apache Iceberg, and Apache Hudi all use Parquet as their underlying file format. If you are storing data in a data lake or reading from a cloud data warehouse, you are reading Parquet files.
Arrow vs Parquet: Key Differences
| Feature | Arrow | Parquet |
|---|---|---|
| Primary use case | In-memory processing, inter-process exchange | Disk storage, data lakes, analytics |
| Compression | None (preserves memory layout) | Excellent (Snappy, Zstd, Gzip) |
| File size | Large (uncompressed columnar) | Small (10–30% of equivalent CSV) |
| Read speed | Fastest (zero-copy memory map) | Fast (decompression required) |
| Write speed | Very fast (no compression) | Moderate (compression overhead) |
| Data lake support | Not standard (Arrow Flight for streaming) | Native (Athena, BigQuery, Spark) |
| Schema embedded | Yes | Yes |
| Tool support | DuckDB, pandas, Polars, PySpark (in-memory) | Universal in data engineering tools |
When to use Arrow
- ✓Passing large datasets between processes without serialization overhead
- ✓Checkpointing in-memory Arrow tables to disk for fast reload
- ✓High-frequency inter-service data exchange using Arrow Flight
- ✓Short-lived intermediate results in a processing pipeline where disk space is not a concern
When to use Parquet
- ✓Storing data long-term in a data lake on S3, GCS, or Azure Blob Storage
- ✓Querying with Athena, BigQuery, Spark, or any cloud data warehouse
- ✓Archiving large datasets to minimise storage costs
- ✓Any scenario where compressed storage and broad tool compatibility matter
Convert between Arrow and Parquet
Convert files instantly in your browser — no upload, no account, no server.
Convert Arrow to Parquet Online
Convert Arrow files to Parquet format directly in your browser. No upload required — your data never leaves your device.
Convert Parquet to Arrow Online
Convert Parquet files to Arrow format directly in your browser. No upload required — your data never leaves your device.
Convert Arrow to CSV Online
Convert Arrow files to CSV format directly in your browser. No upload required — your data never leaves your device.
More format comparisons
CSV vs Parquet
A practical comparison of CSV and Parquet — file size, query performance, compatibility, schema handling, and when to convert between them.
Parquet vs CSV
Parquet offers columnar storage, compression, and embedded schema. CSV is universal and human-readable. Learn the trade-offs and when to convert.
JSON vs CSV
JSON supports nested data and is native to APIs and web applications. CSV is flat, compact, and universally compatible with spreadsheets and databases.
CSV vs JSON
CSV is flat, compact, and universal for spreadsheets and databases. JSON supports nesting and is native to APIs and web applications. Learn when to use each.
Excel vs CSV
Excel supports formulas, charts, and multiple sheets. CSV is plain text, portable, and pipeline-friendly. Learn which to use and when to convert.
CSV vs Excel
CSV is plain text and pipeline-friendly. Excel supports formulas, multiple sheets, and charts. Learn when each is the right choice and how to convert.