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
Scientific Computing and Big Data Analysis with Python and Hadoop

Introduction to streaming execution model

Flink is an open source framework for distributed stream processing that:

  • Provides results that are accurate, even in the case of out-of-order or late-arriving data
  • Is stateful and fault tolerant, and can seamlessly recover from failures while maintaining an exactly-once application state
  • Performs on a large scale, running on thousands of nodes with very good throughput and latency characteristics

The following diagram is a generalized view of stream processing:

Many of Flink's features - state management, handling out-of-order data, flexible windowing – are essential for computing accurate results on unbounded datasets and are enabled by Flink's streaming execution model:

  • Flink guarantees exactly-once semantics for stateful computations. Stateful means that applications can maintain an aggregation or summary of data that has been processed over time, and Flink's checkpointing mechanism ensures exactly-once semantics for an application's state in the event...