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ScyllaDB logoScyllaDB · Feature trail

Database Consolidation and Performance Improvement

1 post · Jul 2026 – Aug 2026

What this trail covers

This post details how Sprig consolidated its data infrastructure from Postgres, ClickHouse, and Redis to ScyllaDB, achieving significant latency improvements and simplifying their architecture. This capability focuses on migrating from multiple specialized databases to a single, high-performance NoSQL solution for complex, data-intensive applications. The post also details strategies for handling out-of-order writes and record versioning during migrations, and the importance of understanding sub-

All ScyllaDB trails
  1. How Sprig Replaced Postgres, ClickHouse & Redis…with 4-8x Better LatencySprig migrated from Postgres, ClickHouse, and Redis to ScyllaDB, achieving 4-8x better latency. The post details the challenges of scaling with Postgres, including write saturation and reader instance limits. It also discusses the need for a low-latency, high-throughput system to handle real-time event processing for AI-powered surveys, processing 20,000-40,000 events/attributes per second. The migration involved handling 15 billion visitors, 75 billion user attributes, and 20 billion event counters, with over 1.3 trillion events processed.
  2. Lessons Learned from Real-World NoSQL Database MigrationsThis post details real-world NoSQL database migration lessons learned, focusing on a streaming bulk load scenario from DynamoDB to ScyllaDB. It highlights the importance of understanding differences between source and target databases, even for similar technologies like ScyllaDB and DynamoDB. The post elaborates on the challenge of out-of-order writes, where newer updates arrive before older ones, and discusses strategies for implementing record versioning and data compression. It also touches upon the ScyllaDB Migrator tool.
  3. What Matters Most for NoSQL MigrationsThis post provides battle-tested tips for planning, executing, and de-risking NoSQL database migrations. It outlines the differences between online and offline migrations, emphasizing the need for schema migration, data migration, and data validation. The post details a migration flow that includes schema tuning, capturing changes via mechanisms like Dynamo Streams or dual-writing, and moving existing data. It highlights the importance of understanding sub-second latency requirements for online migrations and the need for robust data validation to ensure accuracy.