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  • Book Overview & Buying Building Data Streaming Applications with Apache Kafka
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Building Data Streaming Applications with Apache Kafka

Building Data Streaming Applications with Apache Kafka

By : Singh, Kumar
3.8 (4)
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Building Data Streaming Applications with Apache Kafka

Building Data Streaming Applications with Apache Kafka

3.8 (4)
By: Singh, Kumar

Overview of this book

Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur. This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security. By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it.
Table of Contents (14 chapters)
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Data sources

One of the fundamental requirements for any streaming application is that the sources of data should have the ability to produce unbound data in terms of streams. Streaming systems are built for unbound data streams. If source systems have the support for such kinds of data streams, then streaming solutions are the way to go, but if they do not have support for data streams, then either you must build or use prebuilt custom components that build data streams out of those data sources or go for batch-oriented non-streaming-based solutions.

Either way, the key takeaway is that streaming solutions should have data stream producing data sources. This is one of the key design decisions in any streaming application. Any streaming solution or design should ensure that continuous unbound data streams are input to your stream processing engines.
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