Scaling Beyond Postgres: How to Choose a Real-Time Analytical Database

Postgres is a widely used database due to its reliability and flexibility for transactional and basic analytical workloads, but as data volumes and analytical demands grow, its limitations become evident, prompting the need for more specialized solutions. This has led to the rise of cloud data warehouses and real-time analytical databases, each with distinct advantages and trade-offs. Cloud data warehouses like Snowflake and BigQuery offer scalability and ease of use but can be costly and introduce latency, while real-time databases such as ClickHouse and StarRocks provide fast query performance and cost efficiency by co-locating compute and storage. The evolution of analytical systems has seen a return to OLAP principles with modern enhancements, offering businesses a choice between cloud data warehouses, real-time analytical databases, or hybrid approaches to meet their unique needs. As organizations outgrow Postgres, they must consider factors like cost, performance, and complexity to select the right tool for their analytical journey, with real-time databases offering promising capabilities for fast, cost-effective insights without the need for extensive data movement.