Sandon Jacobs is a Developer Advocate at Confluent, based in Raleigh, NC. Sandon has two decades of experience designing and building applications, primarily on the JVM. His data streaming journey began while building data pipelines for real-time bidding on mobile advertising exchanges—and Apache Kafka was the platform to meet that need. Later work in television media and the energy sector included Kafka Streams, Kafka Connect, and provisioning Kafka infrastructure with various infrastructure as code frameworks. https://speakerdeck.com/sandonjacobs 2026-04-14 12:15:54 -0400 Delivered at Devoxx Greece 2026 in Athens. Apache Kafka as a Queue: Bridging Event Streaming and Point-to-Point Messaging Apache Kafka has established itself as the leading platform for event streaming, yet architects and engineers have often turned to other systems to fulfill point-to-point messaging needs. With KIP-932—Queues for Kafka—Kafka now natively supports queue-like semantics. In this session, we’ll dive into the cooperative consumption model that brings message-level acknowledgments to Kafka via the new ShareConsumer API. We’ll trace the journey of a message and demonstrate how developers can programmatically manage message state. Queue-driven tasks are often long-running, with a non-deterministic return time - and the API has this use case covered, as well. Observability is essential in distributed systems - so we’ll also explore the metrics and configuration options that provide essential visibility. Finally, we’ll walk through some sample code - because slides never make it to production. Queues for Kafka give developers a seamless way to meet messaging requirements using the Kafka protocol—without altering how messages are produced. This solution delivers point-to-point messaging with all the core guarantees of Apache Kafka, including durable storage, horizontal scalability, and fault tolerance. Discover how “queues the Kafka way” can elevate your messaging architecture. Find the talk here -> https://bit.ly/48QhDLT Delivered at Devoxx Greece 2026 in Athens. Apache Kafka as a Queue: Bridging Event Streaming and Point-to-Point Messaging Apache Kafka has established itself as the leading platform for event streaming, yet architects and engineers have often turned to other systems to fulfill point-to-point messaging needs. With KIP-932—Queues for Kafka—Kafka now natively supports queue-like semantics. In this session, we’ll dive into the cooperative consumption model that brings message-level acknowledgments to Kafka via the new ShareConsumer API. We’ll trace the journey of a message and demonstrate how developers can programmatically manage message state. Queue-driven tasks are often long-running, with a non-deterministic return time - and the API has this use case covered, as well. Observability is essential in distributed systems - so we’ll also explore the metrics and configuration options that provide essential visibility. Finally, we’ll walk through some sample code - because slides never make it to production. Queues for Kafka give developers a seamless way to meet messaging requirements using the Kafka protocol—without altering how messages are produced. This solution delivers point-to-point messaging with all the core guarantees of Apache Kafka, including durable storage, horizontal scalability, and fault tolerance. Discover how “queues the Kafka way” can elevate your messaging architecture. Find the talk here -> https://bit.ly/48QhDLT Thu, 23 Apr 2026 00:00:00 -0400 https://speakerdeck.com/sandonjacobs/devoxx-greece-2026-apache-kafka-as-a-queue-bridging-event-streaming-and-point-to-point-messaging https://speakerdeck.com/sandonjacobs/devoxx-greece-2026-apache-kafka-as-a-queue-bridging-event-streaming-and-point-to-point-messaging Delivered at Arc of AI Conference 2026 - Austin, Texas. Your shiny, new, cutting-edge RAG microservice is only as smart as its context. And if that context is refreshed by a slow, batch-driven job, your AI is essentially answering today’s critical questions by consulting yesterday’s equivalent of a stale newspaper. It’s time to transition your RAG architecture from batch dependence to streaming certainty. Let’s discuss a “streams-first” approach to building data pipelines with fresh context. We’re using Apache Kafka and Apache Flink to build the always-on knowledge backbone your RAG microservices deserve. We’ll focus on the foundational engineering practices that guarantee reliability and access to real-time data: * Kafka as the data substrate: Data streams based on a fault-tolerant, high-throughput source of truth to capture every critical change across your organization. * Flink’s Real-Time Prep: Leveraging Flink for stateless transformation, stateful contextual enrichment and streamlined chunking—performing the heavy lifting as data arrives. * Production-Grade Guardrails: Implementing crucial patterns like Exactly-Once Semantics (EOS) for data consistency and establishing a Dead Letter Queue (DLQ) strategy for reliable error handling. Join this session for a discussion of the core data principles needed to build truly resilient RAG microservices where the knowledge base is always measured in seconds, not days. Delivered at Arc of AI Conference 2026 - Austin, Texas. Your shiny, new, cutting-edge RAG microservice is only as smart as its context. And if that context is refreshed by a slow, batch-driven job, your AI is essentially answering today’s critical questions by consulting yesterday’s equivalent of a stale newspaper. It’s time to transition your RAG architecture from batch dependence to streaming certainty. Let’s discuss a “streams-first” approach to building data pipelines with fresh context. We’re using Apache Kafka and Apache Flink to build the always-on knowledge backbone your RAG microservices deserve. We’ll focus on the foundational engineering practices that guarantee reliability and access to real-time data: * Kafka as the data substrate: Data streams based on a fault-tolerant, high-throughput source of truth to capture every critical change across your organization. * Flink’s Real-Time Prep: Leveraging Flink for stateless transformation, stateful contextual enrichment and streamlined chunking—performing the heavy lifting as data arrives. * Production-Grade Guardrails: Implementing crucial patterns like Exactly-Once Semantics (EOS) for data consistency and establishing a Dead Letter Queue (DLQ) strategy for reliable error handling. Join this session for a discussion of the core data principles needed to build truly resilient RAG microservices where the knowledge base is always measured in seconds, not days. Tue, 14 Apr 2026 00:00:00 -0400 https://speakerdeck.com/sandonjacobs/stop-answering-todays-questions-with-yesterdays-data-low-latency-rag-with-kafka-and-flink https://speakerdeck.com/sandonjacobs/stop-answering-todays-questions-with-yesterdays-data-low-latency-rag-with-kafka-and-flink