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.