Refactor a synchronous delivery workflow so services own their data, publish asynchronous updates, reject duplicate messages, and use Polly retries, circuit breakers, rate limits, and timeouts without allowing one dependency failure to congest the whole system.
A customer activity view updates when purchases arrive but stays stale after a profile change. Learn how to rekey records, choose KStream and KTable semantics correctly, build a table-to-table join, protect state with changelog topics, handle late data, and test recovery before production.
A growing Kafka cluster stays overloaded after new brokers join because existing partitions do not move automatically. Learn how to rebalance safely, stage upgrades, monitor broker and client health, tune latency against durability, and select a disaster-recovery model using RTO and RPO.
Move a customer-data Kafka proof of concept into enterprise production by separating KRaft controllers from brokers, choosing an on-premises, managed, or hybrid deployment, securing every communication path, enforcing topic-level access, and protecting data at rest.
Build a complete Kafka workflow for order events, from serialization and producer retries to consumer groups, rebalancing, offset commits, database updates, and recovery. The focus is preventing silent loss, duplicate processing, broken ordering, and unstable consumers.
Learn how to design Kafka topics for an inventory pipeline that preserves per-item ordering, scales consumer processing, survives broker failures, and retains the right data without treating Kafka like a general-purpose database.
Redesign a synchronous order workflow as a Kafka event flow, assign clear responsibilities to producers, brokers, consumers, controllers, and supporting tools, then test the consistency, retry, replay, contract, and operational risks before production
Learn how to break a back-end system into practical components, choose storage, add load balancing, caching, queues, partitioning, replication, and real-time communication while making tradeoffs explicit.
Learn how Kafka moves messages from producers into partitioned logs and out to consumers, including batching, acknowledgments, partition keys, offsets, and consumer groups.
Learn how NoSQL databases fit distributed systems, how CAP affects consistency and availability, and when document, graph, wide-column, or key value stores make sense.