Parquet vs ORC
A side-by-side feature comparison.
Summary
Parquet and ORC are the two columnar file formats that dominate analytics. They have nearly identical capabilities — column-grouped storage, multiple compressors, predicate pushdown, schema evolution — but Parquet won mindshare outside Hive and is the safer default in 2026.
Feature comparison
| Feature | Apache Parquet | Apache ORC |
|---|---|---|
| Origin | Twitter / Cloudera | Hortonworks |
| Compression | Snappy / GZIP / Zstd / LZ4 | Zstd / Snappy / Zlib / LZ4 |
| Predicate pushdown | Yes | Yes |
| Index granularity | Row group | Stripe + index |
| Adoption | Industry-wide | Hive ecosystem |
| Schema evolution | Yes | Yes |
About Apache Parquet
Parquet is the dominant columnar data file format for analytics. Each file groups rows into row-groups and stores each column as its own compressed page, allowing query engines to read only the columns they need. DuckDB, Spark, BigQuery, Snowflake and pandas all read and write Parquet natively.
Read the full Apache Parquet guide →
About Apache ORC
ORC (Optimized Row Columnar) is a Hadoop-ecosystem columnar format from Hortonworks, designed for Hive workloads. Like Parquet, it stripes data column-by-column with rich indexing and statistics, but with a different row-group layout that often performs better on Hive itself. Outside the Hive world, Parquet is more common.
Read the full Apache ORC guide →
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