14h ago · 5 min read · Apache Iceberg serves as the ideal durable event store for event-driven architectures. Unlike Kafka—which has limited retention and no SQL query interface—Iceberg on S3 retains events indefinitely, supports time travel to replay any historical period...
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2d ago · 13 min read · Event-driven microservices promise loose coupling and real-time responsiveness. In practice, they deliver something else: hundreds of lines of consumer boilerplate, hand-rolled state management, and retry logic that nobody wants to maintain. If you h...
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2d ago · 2 min read · Context In the enterprise-stack-tutorial project I wired Apache Kafka into a Spring Boot teaching monolith: the same service that exposes a JWT-secured REST API and persists with JPA also publishes integration events when catalog data changes, and in...
Join discussion3d ago · 12 min read · Hertz was a $9.8 billion company running on a 30-year-old technology platform. The CEO said that publicly. Underneath it: 1,800 IT systems, six database vendors, 30 rental processing systems, and a core built on IBM AS/400 mainframes running COBOL. A...
Join discussion6d ago · 5 min read · Spry with Apache Kafka: Building Event-Driven Dart Applications Integrate your Spry Dart applications with Apache Kafka—the distributed streaming platform—to build scalable, event‑driven microservices with fault‑tolerant message processing. Apache ...
Join discussionMar 25 · 13 min read · Spry GraphQL Subscriptions: Building Real‑Time Data Streaming Applications Estimated reading time: 20–30 minutes Introduction GraphQL subscriptions bring real‑time capabilities to your APIs, allowing clients to receive live updates when data changes....
Join discussionMar 20 · 1 min read · Eventual consistency is the fundamental trade-off of event-driven systems: this guide covers strategies for making temporarily inconsistent systems acceptable to users, from optimistic feedback to reconciliation patterns. What you'll learn CAP Theor...
Join discussionMar 20 · 1 min read · The dual-write problem (saving to the DB and publishing to Kafka/SQS atomically) is solved by the Outbox Pattern: write the event to an outbox table in the same DB transaction, then use Debezium CDC for reliable publication. What you'll learn Il pro...
Join discussionMar 20 · 1 min read · In a distributed system with at-least-once delivery, the same message may be received more than once. Consumer idempotency is the defence: deduplication with idempotency keys, exactly-once processing with database transactions and inbox/outbox. What ...
Join discussionMar 20 · 1 min read · The DLQ is the essential safety net for any async system: it captures unprocessable messages after the maximum retry count, enables error analysis and selective re-processing without data loss. What you'll learn Ispezionare e Reprocessare dalla DLQ ...
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