Benefits of Apache Parquet Format in big fata

Benefits of Parquet Format

1. Columnar Storage

   • Efficient for analytics and read-heavy workloads.
   • Only required columns are read into memory.

2. Highly Compressed

   • Supports efficient compression algorithms (Snappy, GZIP, Brotli).
   • Smaller file size compared to row-based formats like CSV/JSON.

3. Splittable & Scalable

   • Files can be split and read in parallel, improving speed in distributed systems like Hadoop/Spark.

4. Schema Evolution

   • Supports adding new columns without breaking existing data pipelines.

5. Efficient for Queries

   • Works well with SQL engines like Hive, Presto, Trino, Athena, BigQuery.

6. Better IO Performance

   • Reduces disk and network IO by avoiding unnecessary data reads.

7. Interoperable

   • Supported across multiple languages and platforms (Python, Java, Spark, Hive, AWS, GCP, etc.).

8. Self-describing Format

   • Stores schema as metadata within the file itself — no need for external schema definitions.

9. Great with Partitioning

   • When used with tools like Hive/Spark, supports directory-based partitioning, improving query performance.

10. Ideal for Lakehouse/Data Lake

• Common choice for Delta Lake, Iceberg, Hudi — supports ACID on Parquet.