Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
4
Scientific Computing and Big Data Analysis with Python and Hadoop
Index

Data processing using the DataStream API


It is crucial to have robust analytics in place to process real-time data. This is more important for domains that are data-driven. Flink enables you to do real-time analytics using its DataStream API. This streaming data processing API helps you cater to Internet of Things (IoT) applications and store, process, and analyze data in real time or near real time.

In the following sections, let's examine each of the elements related to the DataStream API:

  • Execution environment
  • Data sources
  • Transformations
  • Data sinks
  • Connectors

Execution environment

To write a Flink program, you need an execution environment. You can use an existing environment or create a new environment.

Based on your requirements, Flink allows you to use an existing Flink environment, create a local environment, or create a remote environment.

Use thegetExecutionEnvironment()command to accomplish different tasks based on your requirement:

  • To execute on a local environment in an IDE, it starts...