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Real-Time Big Data Analytics

You're reading from   Real-Time Big Data Analytics Design, process, and analyze large sets of complex data in real time

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781784391409
Length 326 pages
Edition 1st Edition
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Author (1):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
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Table of Contents (12) Chapters Close

Preface 1. Introducing the Big Data Technology Landscape and Analytics Platform FREE CHAPTER 2. Getting Acquainted with Storm 3. Processing Data with Storm 4. Introduction to Trident and Optimizing Storm Performance 5. Getting Acquainted with Kinesis 6. Getting Acquainted with Spark 7. Programming with RDDs 8. SQL Query Engine for Spark – Spark SQL 9. Analysis of Streaming Data Using Spark Streaming 10. Introducing Lambda Architecture Index

Coding our first Spark SQL job


In this section, we will discuss the basics of writing/coding Spark SQL jobs in Scala and Java. Spark SQL exposes the rich DataFrame API (http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.DataFrame) for loading and analyzing datasets in various forms. It not only provides operations for loading/analyzing data from structured formats such as Hive, Parquet, and RDBMS, but also provides flexibility to load data from semistructured formats such as JSON and CSV. In addition to the various explicit operations exposed by the DataFrame API, it also facilitates the execution of SQL queries against the data loaded in the Spark.

Let's move ahead and code our first Spark SQL job in Scala and then we will also look at the corresponding implementation in Java.

Coding a Spark SQL job in Scala

In this section, we will code and execute our first Spark SQL Job using Scala APIs.

It is our first Spark SQL job, so we will make it simple and use some sample...

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