Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale

Neha Narkhede, Gwen Shapira, Todd Palino

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商品描述

Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.

Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.

  • Understand publish-subscribe messaging and how it fits in the big data ecosystem.
  • Explore Kafka producers and consumers for writing and reading messages
  • Understand Kafka patterns and use-case requirements to ensure reliable data delivery
  • Get best practices for building data pipelines and applications with Kafka
  • Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks
  • Learn the most critical metrics among Kafka’s operational measurements
  • Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems

商品描述(中文翻譯)

每個企業應用程式都會產生數據,無論是日誌訊息、指標、使用者活動、發出的訊息或其他內容。如何移動這些數據幾乎和數據本身一樣重要。如果你是一位應用程式架構師、開發人員或製程工程師,而且對於 Apache Kafka 這個開源流式平台還不熟悉,這本實用指南將教你如何使用它來處理即時數據流。

Confluent 和 LinkedIn 的工程師負責開發 Kafka,他們將解釋如何部署生產 Kafka 集群、編寫可靠的事件驅動微服務,以及使用這個平台構建可擴展的流式處理應用程式。通過詳細的示例,你將學習 Kafka 的設計原則、可靠性保證、關鍵 API 和架構細節,包括複製協議、控制器和存儲層。


  • 了解發布-訂閱消息傳遞方式及其在大數據生態系統中的應用。

  • 探索 Kafka 的生產者和消費者,用於編寫和讀取訊息。

  • 了解 Kafka 的模式和使用案例需求,以確保可靠的數據傳遞。

  • 獲取使用 Kafka 構建數據管道和應用程式的最佳實踐。

  • 在生產環境中管理 Kafka,並學習執行監控、調優和維護任務的方法。

  • 學習 Kafka 最重要的運營測量指標。

  • 探索 Kafka 的流式傳遞能力,使其成為流式處理系統的理想來源。