What’s the difference between Apache Parquet vs AVRO

Apache Avro and Apache Parquet are two open file formats pivotal in big data processing, each tailored to different use cases and system requirements. Avro, a row-oriented format, is ideal for transactional and streaming data platforms due to its ability to efficiently store records with balanced read and write performance, making it suitable for online transactional processing (OLTP) systems. Its self-describing nature allows for seamless schema evolution, supporting multiple programming languages without the need for code generation. In contrast, Parquet is a column-oriented format optimized for data analytics platforms, providing fast query performance and efficient data compression by storing data elements from each column together, which is particularly beneficial for data warehouses and environments where storage scalability is a concern. Both formats support schema evolution and integrate with modern data architectures like Trino and Iceberg, contributing to the evolving landscape of open data lakehouses that combine the analytical capabilities of data warehouses with the scalability of data lakes. The choice between Avro and Parquet hinges on whether the application requires efficient record processing or data aggregation, with Avro excelling in dynamic environments and Parquet in analytics-driven scenarios.