Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Table of Contents (18 chapters)
Big Data Analytics
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
Index

Why Datasets and DataFrames?


The simple answer is: speed and ease of use. DataFrames provide the benefits of optimization, speed, automatic schema discovery, working with multiple sources, and multiple languages support; they read less data, and provide inter-operability between RDDs. Let's delve into these concepts.

Optimization

Catalyst provides optimization for DataFrames. It provides two optimizations:

  • Predicate push down to sources, to read only the data needed

  • Creates a physical plan for execution and generates JVM bytecode that is better optimized than handwritten code

The DataFrame is not defining Directed Acyclic Graph (DAG) as in the case of RDDs. Abstract Syntax Trees (AST) are created, which the catalyst engine will parse, check, and improve using both rules-based optimization and cost-based optimization.

Figure 4.4 shows the phase of query planning in Spark Sql. So, any DataFrame, Dataset operation or SQL query will follow the same optimization path to create a physical plan and...