Stream Processing with Apache Flink

Get started with Apache Flink, the open source framework that enables you to process streaming data—such as user interactions, sensor data, and machine logs—as it arrives. With this practical guide, you’ll learn how to use Apache Flink’s stream processing APIs to implement, continuously run, and maintain real-world applications.

Format: Paperback / softback
Length: 220 pages
Publication date: 23 April 2019
Publisher: O'Reilly Media, Inc, USA

Get started with Apache Flink, the open-source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing.

Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink's DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards, as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them.

Learn concepts and challenges of distributed stateful stream processing. Explore Flink's system architecture, including its event-time processing mode and fault-tolerance model. Understand the fundamentals and building blocks of the DataStream API, including its time-based and stateful operators. Read data from and write data to external systems with exactly-once consistency. Deploy and configure Flink clusters. Operate continuously running streaming applications.

Apache Flink is an open-source framework for processing data streams in real-time. It is designed to handle large amounts of data and provide high performance and scalability. Flink is used in a wide range of applications, including finance, healthcare, and social media.

One of the key features of Flink is its ability to process data in a parallel and distributed manner. This means that it can handle large data sets that would be too slow to process on a single machine. Flink uses a distributed processing model, which divides the data into smaller chunks and distributes them across multiple machines. Each machine processes a subset of the data and then sends the results back to the central processing unit (CPU) for further processing.

Another feature of Flink is its ability to handle complex data types and operations. Flink supports a wide range of data types, including primitive types such as int, long, and float, as well as more complex types such as arrays, maps, and user-defined types. It also supports a wide range of operations, including filtering, mapping, aggregating, and joining.

Flink is also highly customizable and extensible. It provides a wide range of built-in operators and functions, as well as the ability to write custom operators and functions. This allows developers to tailor Flink to their specific needs and requirements.

Flink is also highly fault-tolerant and resilient. It uses a fault-tolerance model that allows it to recover from failures and continue processing data. Flink uses a distributed checkpointing mechanism to ensure that the data is consistent across all machines. If a machine fails, the checkpointing mechanism ensures that the data is restored to the correct state on another machine.

Flink is also highly scalable. It can handle data streams of any size and can scale up or down based on the requirements of the application. Flink can be deployed on a variety of platforms, including cloud platforms such as Amazon Web Services (AWS) and Microsoft Azure, as well as on-premises data centers.

In conclusion, Apache Flink is a powerful and flexible open-source framework for processing data streams in real-time. It is designed to handle large amounts of data and provide high performance and scalability. Flink is used in a wide range of applications and is highly customizable and extensible. It is also highly fault-tolerant and resilient and can be deployed on a variety of platforms.

Weight: 550g
Dimension: 178 x 231 x 17 (mm)
ISBN-13: 9781491974292