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

Apache Spark

Apache Spark is a unified distributed computing engine across different workloads and platforms. Spark can connect to different platforms and process different data workloads using a variety of paradigms such as Spark Streaming, Spark ML, Spark SQL, and Spark Graphx.

Apache Spark is a fast in-memory data processing engine with elegant and expressive development APIs, which allow data workers to efficiently execute streaming machine learning or SQL workloads that require fast interactive access to data sets. 

Additional libraries built on top of the core allow the workloads for streaming, SQL, graph processing, and machine learning. SparkML, for instance, is designed for data science and its abstraction makes data science easier.

Spark provides real-time streaming, queries, machine learning, and graph processing. Before Apache Spark, we had to use different technologies for different types of workloads. One for batch analytics, one for interactive queries, one for real-time streaming...