Apache Parquet is a modern, open-source, columnar storage file format optimized for analytical workloads. Designed to efficiently handle large-scale, complex data in distributed systems, Parquet has become the default format in data engineering and analytics pipelines across platforms like Spark, Hadoop, AWS, and Azure.
History and Origins
Parquet was developed in 2013 as a joint effort between Twitter and Cloudera, built on Google’s Dremel paper and inspired by its internal columnar storage format. The goal was to create a format that could store nested data structures efficiently, support schema evolution, and deliver high-performance analytical reads—all in an open standard.
Since its release, Parquet has become an Apache project and is now a core component of the Hadoop ecosystem, often used with Apache Arrow, Apache Hive, Apache Drill, and Apache Impala.
How Parquet Works
Parquet stores data in a columnar format, meaning that all values of a given column are stored together, rather than row by row. This approach enables better compression, faster filtering, and more efficient reads when only a subset of columns is needed.
Key Concepts:
- Columnar Storage: Enables reading only the columns you need for a query, drastically reducing I/O.
- Schema: Parquet files include a self-describing schema stored in the metadata, making it easier to manage and evolve data structures.
- Data Pages: Data is stored in pages grouped into row groups, each containing values for a subset of the dataset’s rows.
- Encodings: Uses dictionary and run-length encoding to reduce file size without sacrificing performance.
- Compression: Supports various codecs like Snappy, GZIP, Brotli, and LZO for efficient storage.
Benefits of Using Parquet
- Highly Compressed: Parquet’s columnar layout combined with modern compression yields smaller file sizes, especially with repeated values.
- Efficient Reads: Column pruning and predicate pushdown reduce read time and memory usage in queries.
- Schema Evolution: Compatible with backward and forward schema changes, making it reliable for changing datasets.
- Nested Data Support: Unlike CSV or JSON, Parquet handles nested structures using a format called repetition and definition levels.
- Platform-Agnostic: Works across cloud platforms (AWS S3, Azure Blob, Google Cloud Storage) and analytics engines (Spark, Presto, Athena).
Parquet vs. Other Formats
| Feature | Parquet | CSV | JSON | Avro |
|---|---|---|---|---|
| Storage Type | Columnar | Row-based | Row-based | Row-based |
| Compression | Excellent | Poor | Poor | Good |
| Schema Support | Yes (in file) | No | Partial | Yes |
| Nested Data | Yes | No | Yes | Yes |
| Best Use Case | Analytics | Exports | APIs | Streaming |
Common Use Cases
- Data Warehousing: Storing large analytical datasets in a compressed, query-efficient format.
- Cloud Analytics: Used with services like AWS Athena, Google BigQuery, Azure Synapse for serverless querying.
- ETL Pipelines: Intermediate storage for high-throughput pipelines using Apache Spark or AWS Glue.
- Machine Learning: As training datasets due to efficient scan performance.
Tools That Support Parquet
- Engines: Apache Spark, Hive, Presto, Trino, Impala, Drill, BigQuery
- Cloud: AWS Athena, S3 Select, Azure Data Lake, Google Cloud Storage
- Languages: Python (pyarrow, pandas), R, Java, Scala, C++
- File Viewers: parquet-tools, DuckDB, DataGrip, Jupyter Notebooks
Best Practices
- Use Snappy compression for a balance between speed and size.
- Keep row group size optimized (e.g., 128MB) for large reads.
- Partition datasets by common filter fields (e.g., date, region) for faster querying.
- Avoid writing many small Parquet files—merge them into fewer large files to avoid performance issues.
Conclusion
Parquet is the preferred format for storing and analyzing massive datasets in a performant, scalable, and cost-effective manner. Its columnar architecture, efficient compression, and compatibility with modern data stacks make it a cornerstone of cloud data engineering.
If your workload involves analytics, reporting, or machine learning, Parquet is often the best default choice for both storage and performance.