Disaster-Recovery

This week’s coverage centers on a persistent friction point for teams running DynamoDB at scale: the constraints of its query model and the architectural decisions those constraints force. AWS documentation and community practice converge on a common theme — DynamoDB’s design tradeoffs are known and intentional, but navigating them in production requires deliberate engineering.

This week’s coverage centers on a single but consequential theme: what it actually takes to recover databases reliably on AWS when those databases span multiple purpose-built services. Alongside the architectural guidance, a quiet operational concern has surfaced in the community — one that practitioners should factor into any recovery plan that depends on vendor support as a backstop.

Two distinct but complementary themes emerge this week: the growing role of graph databases as infrastructure for stateful AI workloads, and the operational complexity of coordinating recovery across polyglot persistence architectures. Both trends reflect a maturation in how organizations think about Neptune — not as a standalone graph store, but as a component embedded in broader, more demanding system designs.