Streaming Data Pipelines with Kafka by Stefan Sprenger

In today’s rapidly evolving digital landscape, data integration is shifting from conventional batch processing methods to the realm of real-time streaming data pipelines. These pipelines hold the power to keep data systems in sync, ensuring that information is always fresh and readily available. Yet, mastering this transformative technology can be a daunting task, requiring expertise in multiple areas. Enter Streaming Data Pipelines with Kafka, your treasured guide to utilizing Kafka for your data pipeline needs.


Out now at Manning.com


Who Is This Book For?

Streaming Data Pipelines with Kafka is crafted for data engineers, software developers, and professionals facing data integration challenges in their workplaces. This comprehensive guide offers a complete introduction to the concepts, development, and deployment of streaming data pipelines, with a strong focus on Apache Kafka, the industry-standard event streaming platform. It equips you not only with foundational knowledge but also with practical skills to implement streaming data pipelines successfully.

Throughout the book, we follow an imaginary e-commerce company on its journey to migrate legacy batch pipelines to a modern streaming architecture, providing real-world context for your learning.

Let’s dive into some of the practical lessons you can expect to find within this book.


Practical Lessons

The Power of Kafka Connectors:

One of the fundamental lessons you’ll learn is the significance of Kafka connectors. These connectors act as the bridge between your data sources and the Kafka pipeline, enabling seamless data ingestion. When dealing with a variety of data sources, like databases, cloud services, or IoT devices, Kafka connectors simplify the integration process, making it easier to capture, transform, and move data in real time.

Stateless vs. Stateful Stream Processing:

Streaming data pipelines often involve processing data on the fly. In the book, you’ll dive deep into the concepts of stateless and stateful stream processing. You’ll discover that stateless operations are suitable for tasks like data mapping and filtering, where each event is processed independently. On the other hand, stateful operations, such as event deduplication or data aggregation, require maintaining state information over time. Understanding when to apply each type of processing is crucial for building efficient and reliable pipelines.

Containerization and Deployment with Docker and Kubernetes:

Modern data pipelines require robust deployment strategies. “Streaming Data Pipelines with Kafka” delves into container technologies like Docker and Kubernetes. You’ll learn how to package your streaming data pipelines into containers for consistent deployment across various environments. This lesson isn’t just about theory; it’s a practical guide to taking your pipelines from development to production with confidence.

Monitoring for Pipeline Health:

Monitoring is an essential aspect of maintaining a healthy streaming data pipeline. Throughout the book, you’ll gain insights into setting up effective monitoring systems. You’ll learn how to track the performance of your connectors, processors, and Kafka clusters. Plus, you’ll understand the importance of alerting mechanisms to detect and address issues in real time. Monitoring isn’t just about collecting data; it’s about ensuring the reliability and availability of your data pipelines.


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Performance Tuning for Low Latency:

Achieving low-latency data processing is a goal for many organizations. “Streaming Data Pipelines with Kafka” equips you with practical strategies for performance tuning. You’ll discover techniques to optimize your pipelines for different latency and throughput requirements. Whether you’re dealing with mission-critical financial data or real-time analytics for IoT applications, these lessons will help you fine-tune your pipelines for peak performance.

Error Handling and Resilience:

Mistakes and errors are inevitable in data pipelines. The book covers the various types of errors you might encounter and provides strategies for handling them gracefully. You’ll learn about fault tolerance mechanisms and how to design resilient pipelines that can recover from failures without data loss. Understanding error handling is crucial for ensuring the reliability of your data infrastructure.


Streaming Data Pipelines with Kafka goes beyond theory, providing you with actionable knowledge to build, deploy, and maintain robust real-time data pipelines. Whether you’re a data engineer, software developer, or a professional dealing with data integration challenges, this book equips you with the skills and know-how to navigate the world of streaming data with confidence.