Today's episode unpacks the profound challenges of data management and consistency that emerge from the evolution towards distributed cloud-native and microservices architectures. We explore how moving from centralized monoliths necessitates sophisticated data strategies, focusing on patterns that address these complexities. We discussed Database per Service, a cornerstone where each microservice owns its private database for independent scalability and autonomy, even though it complicates distributed transactions. This approach naturally extends to Polyglot Persistence, advocating for the use of diverse database technologies tailored to each service's specific needs, optimizing performance but increasing operational complexity. For extreme scalability with massive datasets, Sharding horizontally partitions data, though it introduces significant architectural complexity for cross-shard operations and rebalancing. Crucially, for managing complex, long-lived business transactions across multiple services, the Saga Pattern breaks them into local transactions and uses compensating actions to ensure eventual consistency, avoiding traditional two-phase commits but adding design and debugging complexity. Conversely, the Shared Database pattern is largely identified as an anti-pattern for mature microservices due to its detrimental effects of tight coupling and scalability bottlenecks.