Book Image

Learning Apache Flink

By : Tanmay Deshpande
Book Image

Learning Apache Flink

By: Tanmay Deshpande

Overview of this book

<p>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.</p> <p>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.</p> <p>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.</p>
Table of Contents (17 chapters)
Learning Apache Flink
About the Author
About the Reviewers
Customer Feedback


FlinkML is a library of sets of algorithms supported by Flink that can be used to solve real-life use cases. The algorithms are built so that they can use the distributed computing power of Flink and make predictions or do clustering and so on with ease. Right now, there are only a few sets of algorithms supported, but the list is growing.

FlinkML is being built with the focus on ML developers needing to write minimal glue code. Glue code is code that helps bind various components together. Another goal of FlinkML is to keep the use of algorithms simple.

Flink exploits in-memory data streaming and executes iterative data processing natively. FlinkML allows data scientists to test their models locally, with a subset of data, and then execute them in cluster mode on bigger data.

FlinkML is inspired by scikit-learn and Spark's MLlib, which allows you to define data pipelines cleanly and solve machine learning problems in a distributed manner.

The following is the road map Flink's development...