Real-time analytics API at scale with billions of rows

As data accumulates rapidly in various applications, managing and extracting meaningful insights from billions of rows becomes a significant challenge, especially for real-time analytics. Traditional databases like Postgres or Mongo can handle large volumes of data but struggle with speed when processing complex queries, particularly under medium to high loads. Tinybird offers a solution by optimizing data management and query processes, leveraging Clickhouse® for statistical approximations and real-time pre-aggregations to enhance performance. By designing efficient data schemas, using pre-aggregated tables, denormalizing data, and applying smart indexing and partitioning strategies, Tinybird enables quick iteration and high query-per-second (QPS) rates with reduced latency. For instance, data rollups significantly reduce the query time by narrowing the data scope, maintaining high performance even with large datasets. Tinybird's approach allows for scalable and efficient data handling, minimizing the need for costly and inflexible pre-aggregations and ETLs, thereby supporting high-demand use cases and facilitating seamless integration into CI/CD workflows.