Learning Apache Flink

by Tanmay Deshpande
English | 2017 | ISBN: 1786466228 | 275 Pages | True PDF | 4 MB

Big data developers who are looking to process batch and real-time data on distributed systems. Basic knowledge of Hadoop and big data is assumed. Reasonable knowledge of Java or Scala is expected.

What you will learn:

- Learn how to build end to end real time analytics projects
- Integrate with existing big data stack and utilize existing infrastructure
- Build predictive analytics applications using FlinkML
- Use graph library to perform graph querying and search.
- Understand Flink's - "Streaming First" architecture to implementing real streaming applications
- Learn Flink Logging and Monitoring best practices in order to efficiently design your data pipelines
- Explore the detailed processes to deploy Flink cluster on Amazon Web Services(AWS) and Google Cloud Platform (GCP).

With the advent of massive computer systems, organizations in different domains generate large amounts of data on a real-time basis. The latest entrant to big data processing, Apache Flink, is designed to process continuous streams of data at a lightning fast pace.

This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins with introducing the Apache Flink ecosystem, setting it up and using the DataSet and DataStream API for processing batch and streaming datasets. Bringing the power of SQL to Flink, this book will then explore the Table API for querying and manipulating data. In the latter half of the book, readers will get to learn the remaining ecosystem of Apache Flink to achieve complex tasks such as event processing, machine learning, and graph processing. The final part of the book would consist of topics such as scaling Flink solutions, performance optimization and integrating Flink with other tools such as ElasticSearch.

Whether you want to dive deeper into Apache Flink, or want to investigate how to get more out of this powerful technology, you ll find everything you need inside.

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