Apache Kafka
Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
Apache Kafka in Action
Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and subscribing to streams of records.
- How it is used
- Connects independent services through retained event streams for near-real-time exchange and later replay.
- How deep it goes
- Explains topics, partitions, offsets, replication, clients, and cluster operation, extending to message persistence and governance.
- Examples
- Uses parallel console producers and consumers to send price changes and reread them from the beginning.
- What you can do after
- Operate/configure it by producing and consuming records, designing topics, and planning reliable cluster infrastructure.
Kafka for Architects
An open-source distributed event-streaming platform for publishing, storing, processing, and subscribing to streams of records.
- How it is used
- Positions Kafka as an integration backbone for decoupled services, high-volume event pipelines, and real-time reactions.
- How deep it goes
- Explains components, persistence, replay, and architectural trade-offs, while leaving application code and detailed client APIs aside.
- Examples
- Uses customer address notifications, order processing, log collection, fraud scoring, and IoT telemetry to illustrate possible workloads.
- What you can do after
- Evaluate/understand it as a candidate platform by weighing throughput, reliability, ordering needs, operational complexity, and alternatives.
Kafka Streams in Action, Second Edition
Apache Kafka is a distributed event streaming platform for publishing, storing, and processing streams of records.
- How it is used
- Supports event-driven applications when data comes from multiple places or arrives in volumes requiring distributed data stores.
- How deep it goes
- Explains the publish, store, and process model plus component roles, but stops short of detailed broker internals or implementation code.
- Examples
- Uses ZMart coupon clicks, purchases, customer points, masked sales data, and dashboard processing as concrete event-streaming examples.
- What you can do after
- Build end-to-end event-streaming applications that capture, publish, consume, store, integrate, and transform records.
Read how Kafka Streams in Action, Second Edition explains Apache Kafka →
AI-Powered Developer: Build software with ChatGPT and Copilot
Apache Kafka is a distributed event-streaming platform for building real-time data pipelines and streaming applications.
- How it is used
- Streams asset-location events into an event-driven inventory workflow for real-time monitoring.
- How deep it goes
- Defines Kafka, configures a local Docker deployment, creates topics, and implements a Python consumer; defers broker internals.
- Examples
- Demonstrates Docker Compose, asset_location console commands, JSON location events, and a Python consumer mediated by AssetLocationUpdated.
- What you can do after
- Operate/configure Kafka locally, create and exercise a topic, and route consumed location events through a mediator.
Read how AI-Powered Developer: Build software with ChatGPT and Copilot explains Apache Kafka →
Troubleshooting Java, Second Edition
Kafka is a distributed event-streaming platform that publishes, stores, and delivers records through topics.
- How it is used
- Uses topic inspection to verify event propagation while investigating missing or delayed records in distributed payment flows.
- How deep it goes
- Explains Kafka topic inspection within a multi-tool investigation but stops short of broker, producer, consumer, or storage internals.
- Examples
- Uses a missing one-euro POS payment, trace spans, logs, and Kafka topic evidence as investigative artifacts.
- What you can do after
- Diagnose/debug with Kafka topic inspection alongside traces and logs to locate missing event propagation.
Read how Troubleshooting Java, Second Edition explains Apache Kafka →
Spring in Action, Sixth Edition
Apache Kafka is a distributed event-streaming platform for publishing, storing, and consuming ordered message streams.
- How it is used
- Deploys asynchronous publish/subscribe messaging for scalable, resilient event streams.
- How deep it goes
- Explains cluster, topic and partition architecture, then demonstrates Spring-based sending without detailing consumer implementation.
- Examples
- Uses a Kafka cluster diagram, TacoOrder payloads, the tacocloud.orders.topic topic and localhost:9092 configuration.
- What you can do after
- Apply Kafka through Spring Kafka by configuring brokers and publishing domain objects with KafkaTemplate.
Read how Spring in Action, Sixth Edition explains Apache Kafka →
Related concepts
- ZooKeeperuses · Apache ZooKeeper is a distributed coordination service that provides shared configuration, naming, synchronization, metadata management, and leader-election primitives.
- publish/subscribe modelimplements · The publish-subscribe model is a messaging pattern in which publishers send messages without addressing specific recipients, and subscribers receive messages according to their interests or subscriptions.
- streamingkind of · Streaming is the incremental handling of a sequence of data items, including their processing, transfer, or consumption as they become available.
- distributed commit logkind of · A distributed commit log is a persistent, append-only sequence of ordered records replicated across multiple nodes for later reading.
- Flinkused by · Apache Flink is an open-source distributed processing framework for stateful computation over bounded and unbounded data streams.
- distributed systemkind of · A distributed system is a computing system whose components run across multiple networked processes or machines and coordinate through communication.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.