May 1, 2024
Updated July 8, 2025
15 minute read
Structured Streaming is a powerful tool for processing data streams in Apache Spark. It provides a high-level API that enables developers to build streaming applications with ease. Structured Streaming applications are built on top of Spark SQL, which provides a unified programming model for both batch and streaming data processing. This makes it easy to integrate streaming data processing into existing Spark applications.
Why Learn Structured Streaming?
There are many reasons why you might want to learn Structured Streaming. First, Structured Streaming is a very efficient way to process data streams. It uses a micro-batching approach to process data in small batches, which reduces the latency and overhead associated with traditional batch-based processing. Second, Structured Streaming is a very flexible tool. It can be used to process data from a variety of sources, including Kafka, Flume, and HDFS. Third, Structured Streaming is a very powerful tool. It can be used to perform a wide variety of data processing tasks, including filtering, aggregation, and transformation.
How to Learn Structured Streaming
There are many ways to learn Structured Streaming. You can read the official documentation, take an online course, or read a book. If you are just getting started with Structured Streaming, I recommend taking an online course. There are many great courses available, and they will provide you with a solid foundation in the basics of Structured Streaming.
Online Courses
There are many online courses available that can teach you Structured Streaming. Some of the most popular courses include:
81jhyi|
Find a path to becoming a Structured Streaming. Learn more at:
OpenCourser.com/topic/81jhyi/structured
Reading list
We've selected 22 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Structured Streaming.
Is considered a must-read for anyone working with Apache Spark. It provides a comprehensive overview of Spark's architecture and APIs, including a detailed section on Structured Streaming. Its co-author creator of Spark, lending significant authority. It serves as an excellent reference and learning resource for both beginners and experienced users looking to solidify their understanding of Spark and Structured Streaming.
As a book specifically dedicated to stream processing with Spark, this must-read for those focusing on Structured Streaming. It provides in-depth coverage of the API, its concepts, and practical implementation details. It's an essential resource for moving beyond the basics and truly mastering the art of building streaming applications with Spark.
The second edition of Learning Spark highly recommended book for getting started with Apache Spark, updated to cover Spark 3.0. It provides a clear and practical introduction to Spark's Structured APIs, which are fundamental to Structured Streaming. is widely used in both academic and industry settings for learning the basics of Spark and its core functionalities.
Strong contender for a must-read, particularly for those who prefer learning through practical examples in Java, Python, or Scala. Its coverage of building end-to-end applications, including streaming ingestion, makes it highly relevant. The dedicated chapter on Structured Streaming ingestion ensures that this key topic is covered in a practical context.
Focuses on Apache Spark 3 and covers both batch and stream processing, including Structured Streaming. It explains how to scale Spark for massive datasets and use its structured APIs for data transformations and analytics. The book delves into Spark Streaming's execution model and architecture, providing practical guidance for implementing streaming jobs and applications.
For anyone planning to deploy Structured Streaming applications in production, this book must-read. It addresses the critical aspects of performance tuning, optimization, and scaling Spark jobs. The techniques and best practices covered are directly applicable to ensuring that your streaming pipelines are efficient, cost-effective, and reliable under heavy loads.
While not a Spark book, this is widely considered a must-read for any data professional. Its comprehensive coverage of distributed systems, data processing, and the fundamentals of stream processing provides an essential theoretical foundation that complements the practical knowledge gained from Spark-specific books. Understanding the concepts in this book will make you a more effective Structured Streaming practitioner.
Good entry point for learning Apache Spark 3, including Structured Streaming. It covers the fundamentals of Spark's distributed data processing engine and introduces Structured Streaming for building real-time applications. The book provides real-world examples and code snippets, making it practical for beginners to understand the core concepts and features of Structured Streaming within the broader Spark ecosystem.
The second edition of High Performance Spark is updated for Spark 3.x and beyond, offering the latest best practices for optimizing Spark applications. This is highly relevant for ensuring that Structured Streaming jobs are performant and scalable in production environments. It covers new use cases, code examples, and techniques for working with larger datasets and deploying Spark on modern platforms like Kubernetes.
Provides a deep dive into the concepts and challenges of building large-scale streaming data processing systems. While not specific to Spark Structured Streaming, it offers invaluable knowledge about the underlying principles and patterns of stream processing. It's an advanced read that can significantly deepen your understanding of the complexities involved in designing and implementing robust streaming solutions, which is highly relevant for mastering Structured Streaming at scale.
Provides an overview of the Apache Spark framework and its various libraries, including Spark Streaming and Structured Streaming. It teaches you how to use Spark for big data analysis, covering data processing fundamentals and implementing data stream consumption. While it might not go into extreme depth, it's a good resource for understanding how Structured Streaming fits within the broader Spark ecosystem and for learning basic stream processing implementations.
Aimed at intermediate and advanced readers, this book covers the fundamental Spark components, including Streaming. While it might not focus exclusively on Structured Streaming, it provides detailed coverage of Spark's primary components and offers numerous code walkthroughs. It's a valuable resource for deepening your understanding of Spark's overall architecture and how streaming fits within it.
The second edition of the Kafka guide includes updates on newer features and best practices for deploying and configuring Kafka, which is frequently used with Spark Structured Streaming. It provides essential knowledge for setting up reliable data ingestion pipelines for your streaming applications. This updated version valuable reference for anyone integrating Kafka with Structured Streaming.
While not directly about Spark Structured Streaming, Kafka widely used messaging system that often serves as a data source for Spark Streaming applications. provides a comprehensive guide to Kafka, covering its design principles, architecture, and how to build scalable stream-processing applications with it. Understanding Kafka is highly beneficial for anyone working with real-time data pipelines that feed into Structured Streaming.
Provides an overview of Spark and related big-data technologies, including Spark Streaming. While it may not be exclusively about Structured Streaming, it offers a high-level view of Spark's ecosystem and its capabilities for big data analytics, including processing streaming data. It's suitable for time-pressed professionals looking for a single source to understand Spark's various components.
Explores patterns for large-scale data analysis with Spark, including machine learning and potentially aspects relevant to processing streaming data for analytical purposes. While not a primary resource for learning Structured Streaming implementation, it can provide insights into applying analytical techniques to data processed via streaming, making it relevant for those interested in the data science aspects of streaming.
This cookbook provides practical recipes and code samples for various Spark functionalities, including real-time streaming. While it might not offer in-depth theoretical explanations, it's a useful reference for quickly finding solutions to common tasks in Spark, including those related to streaming data processing. It can be a good supplementary resource for hands-on learning.
Focuses on Apache Spark 2.x and covers its various components, likely including Spark Streaming and potentially an introduction to Structured Streaming as it was emerging. While not covering the latest Spark 3 features, it can still provide valuable context on the evolution of Spark's streaming capabilities and advanced techniques applicable to stream processing. It's more valuable as additional reading for historical context and foundational concepts. While published before the widespread adoption of Structured Streaming, it offers insights into the Spark ecosystem that are still relevant.
Covers the latest version of Apache Spark, Spark 3.x, focusing on the enhancements and new features introduced in Structured Streaming.
Provides a hands-on approach to building real-time data applications with Structured Streaming, including topics such as data ingestion, transformations, and optimizations.
Covers various performance optimizations for Apache Spark, including techniques for optimizing structured streaming applications.
Provides a comprehensive overview of structured streaming with Apache Spark, covering the fundamentals, architecture, and best practices for building streaming applications. However, it is only available in Chinese.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/81jhyi/structured
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