Designing Data-Intensive Applications

Design distributed data systems using proven patterns for partitioning and replication to ensure robustness and scalability. This approach covers consensus, streaming, and transactions across storage models to address correctness under failure.

The best design patterns for scalable stream processing include event sourcing and CQRS to build robust distributed architectures. These patterns provide practical implementation guidance for real-world data-intensive applications.

Consensus in distributed systems maintains transactional correctness by applying patterns that address failures and performance trade-offs. This ensures robust data replication and partitioning across storage models.

You need partitioning and CQRS for data-intensive applications when building scalable architectures that require robust distributed data handling. These patterns provide real-world decision guides for production readiness.

Analyze failure modes in a transactional workflow by identifying the involved distributed data patterns and their specific failure points. This provides diagnostic checks to evaluate production readiness and performance trade-offs.