Hive vs Iceberg: Choosing the best table format for your analytics workload

Apache Hive and Apache Iceberg are two open-source technologies used for managing large datasets, but they differ significantly in their architecture and capabilities. Hive, built on top of Hadoop, allows users to query and analyze big data with a SQL-like interface and is known for its ease of use for non-programmers. However, it faces challenges such as slow file operations, inefficient data manipulation language (DML) operations, costly schema changes, and lack of inherent ACID compliance. Iceberg, designed with modern cloud infrastructure in mind, addresses these limitations by offering efficient updates and deletes, snapshot isolation, and partitioning. It supports full DML on cloud storage, in-place schema changes, and ACID-compliant transactions, making it suitable for different use cases like latency-sensitive data applications, collaborative workflows, root cause analysis, and compliance needs. Migrating from Hive to Iceberg requires careful planning and consideration of specific use cases to optimize data performance effectively.