Data Modeling and Storage Strategy Questions

Covers the end to end considerations for modeling, storing, and operating application data at scale. Topics include choosing between relational, document, key value, wide column, and graph stores based on access patterns; trade offs between normalization and denormalization; indexing and query optimization; partitioning and sharding strategies (range, hash, consistent hashing); replication topologies and failover patterns; consistency models and transaction semantics including strong consistency, eventual consistency, isolation levels, and distributed transactions; caching strategies and cache invalidation; event sourcing and command query responsibility segregation for auditability and scale; analytics versus operational storage patterns, data warehousing and lake options, and change data capture for near real time pipelines. Also includes operational concerns such as backup and restore, retention and archival policies, geo distribution and latency considerations, cost and capacity planning, security and compliance concerns for sensitive data, and migration strategies between storage technologies.