ddia-systems

Design distributed data systems by evaluating storage engines, replication models, partitioning strategies, and transaction guarantees using the DDIA framework. This ensures robust, scalable data platforms tailored to your specific workload requirements.

LSM-trees optimize write throughput by appending data sequentially for write-heavy workloads, while B-trees maintain in-place updates to provide predictable read latency for read-heavy OLTP and OLAP operations.

Select single-leader replication for straightforward consistency and transaction support, or choose leaderless and multi-leader topologies to maximize write availability and handle network partitions while applying CRDTs for conflict resolution.

Compare key-range and hash partitioning to distribute data evenly; hash partitioning mitigates hotspot risks effectively while key-range enables efficient range queries, requiring careful rebalancing and secondary index planning.

Apply Serializable Snapshot Isolation (SSI) for strict consistency without locking overhead in read-heavy environments, and use sagas with idempotent operations to manage distributed consistency across long-running transactions and avoid anomalies.

Stream processing with Change Data Capture (CDC) and event sourcing works for building derived data stores by leveraging stream-table duality to design end-to-end data pipelines that continuously update derived views.