When to use columnar databases over Postgres, MySQL, or MongoDB

When developing applications, traditional relational databases like Postgres and MySQL, or NoSQL databases such as MongoDB, are commonly used for their general-purpose capabilities and transactional features. However, for real-time analytics and large-scale data queries, columnar databases like ClickHouse, Pinot, and Druid significantly outperform traditional databases due to their optimization for online analytical processing (OLAP). These column-oriented databases store data in columns rather than rows, enhancing data locality and allowing for faster query processing, especially for aggregation and filtering tasks typical in analytics. Concepts such as compression, vectorization, caching, sorting, and parallelization further optimize performance by ensuring data is processed swiftly and efficiently. Additionally, features like probabilistic data structures, fast writes with log-structured merge-trees (LSMTs), incremental materializations, and specialized functions for time series data make columnar databases particularly suitable for handling big data and real-time analytics. While they may present management challenges due to their capacity to handle vast amounts of data, columnar databases offer a compelling alternative for projects requiring extensive analytical capabilities, with platforms like Tinybird simplifying their integration and use.