Storage Formats Compared - Interactive Visualization

Choosing the right storage format is one of the most consequential decisions in a data engineering system. Columnar formats like Parquet and ORC achieve 10–50x compression over CSV by storing values of the same column together, enabling predicate pushdown and projection pruning. Row-oriented formats like Avro excel at write-heavy workloads and schema evolution. This demo lets you compare all major formats on the metrics that matter in production.

• Parquet: columnar, Snappy/Zstd compression, ideal for analytics queries that read few columns
• ORC: columnar like Parquet, optimized for Hive/Spark, built-in bloom filters and indexes
• Avro: row-oriented, compact binary, best-in-class schema evolution with a JSON schema registry
• CSV: human-readable, no compression, no types - avoid in production pipelines
• Columnar formats reduce I/O by 5–20x for analytical queries that only touch a few columns
• Schema evolution: Avro handles field additions/removals; Parquet is more rigid but supports nullable fields

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