Big data storage formats are specialized file structures designed to efficiently store, compress, and query massive datasets in distributed computing environments. They fall into two primary paradigms: columnar formats (Parquet, ORC, Arrow) optimized for analytics with selective column reads and superior compression, and row-based formats (Avro, CSV, JSON) suited for write-heavy workloads and full-row access. Beyond basic file formats, open table formats (Delta Lake, Apache Iceberg, Apache Hudi) add a critical metadata layer that enables ACID transactions, schema evolution, time travel, and enterprise-grade reliability on top of immutable data files. Understanding the trade-offs between compression ratios, query performance, schema flexibility, and transactional capabilities is essential for architecting modern data platforms that balance cost, speed, and scalability.
What This Cheat Sheet Covers
This topic spans 21 focused tables and 137 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Storage Paradigms
| Paradigm | Example | Description |
|---|---|---|
SELECT revenue FROM salesreads only revenue column | • Stores data by column rather than row • enables selective column reads, superior compression (10-100x better than row formats), and vectorized processing &bull • Analytics queries scan fewer bytes &bull • Ideal for OLAP workloads | |
INSERT INTO users VALUES (...)writes entire row at once | • Stores complete records sequentially as rows • optimized for transactional writes, full-row retrieval, and frequent updates &bull • Better for OLTP workloads &bull • Poor compression compared to columnar |