Key FeaturesBuild your expertize in processing real-time data with Apache Flink and its ecosystemGain insights into the working of all components of Apache Flink such as FlinkML, Gelly, and Table API filled with real world use casesExploit Apache Flinks capabilities like distributed data streaming, in-memory processing, pipelining and iteration operators to improve performance.Solve real world big-data problems with real time in-memory and disk-based processing capabilities of Apache Flink.Book DescriptionWith 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, youll find everything you need inside.What you will learnLearn how to build end to end real time analytics projectsIntegrate with existing big data stack and utilize existing infrastructureBuild predictive analytics applications using FlinkMLUse graph library to perform graph querying and search.Understand Flinks - Streaming First architecture to implementing real streaming applicationsLearn Flink Logging and Monitoring best practices in order to efficiently design your data pipelinesExplore the detailed processes to deploy Flink cluster on Amazon Web Services(AWS) and Google Cloud Platform (GCP).About the AuthorTanmay Deshpande is a Hadoop and big data evangelist. He currently works with Schlumberger as a Big Data Architect in Pune, India. He has interest in a wide range of technologies, such as Hadoop, Hive, Pig, NoSQL databases, Mahout, Sqoop, Java, cloud computing, and so on. He has vast experience in application development in various domains, such as oil and gas, finance, telecom, manufacturing, security, and retail. He enjoys solving machine-learning problems and spends his time reading anything that he can get his hands on. He has great interest in open source technologies and has been promoting them through his talks. Before Schlumberger, he worked with Symantec, Lumiata, and Infosys. Through his innovative thinking and dynamic leadership, he has successfully completed various projects.He regularly blogs on his website http://hadooptutorials.co.in. You can connect with him on LinkedIn at https://in.linkedin.com/in/deshpandetanmay.He has also authored Mastering DynamoDB, published in August 2014, DynamoDB Cookbook, published in September 2015, Hadoop Real World Solutions Cookbook-Second Edition, published in March 2016, Hadoop: Data Processing and Modelling, published in August, 2016, and Hadoop Blueprints, published in September 2016, all by Packt Publishing.Table of ContentsIntroduction to Apache FlinkData Processing Using the DataStream APIData Processing Using the Batch Processing APIData Processing Using the Table APIComplex Event ProcessingMachine Learning Using FlinkMLFlink Graph API - GellyDistributed Data Processing with Flink and HadoopDeploying Flink on CloudBest Practices