data-infrastructure-at-scale

To scale data infrastructure from MVP to production, plan staged migrations using sharding, replication, and caching, while evolving ETL/ELT pipelines to handle increasing data volumes safely. This prevents full system rewrites by designing distributed systems architectures early.

Selecting databases and caches for high-throughput pipelines requires analyzing read/write patterns and planning capacity across stages from prototype to distributed systems. Compare data lake architectures against message queues to match your specific throughput requirements.

Plan a migration to a distributed data lake architecture by identifying bottlenecks in your current setup, then proposing a phased migration plan that introduces sharding and read/write separation to evolve systems safely.

Yes, you can use this approach for both real-time streaming analytics and ETL pipelines by integrating message queues and caching layers into your data infrastructure. This supports scalable data movement across prototype and production environments.

You need sharding and replication in your data infrastructure when scaling beyond a single node's capacity for read/write throughput. These mechanisms distribute load and ensure high availability as data volumes grow from MVP to production.