Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

SparkSQL CLI


Spark provides SparkSQL CLI to work with the Hive metastore service in local mode and execute queries input from the command line.

You can start the Spark-SQL CLI as follows:

./bin/spark-sql

Configuration of Hive is done by placing your hive-site.xml, core-site.xml, and hdfs-site.xml files in conf/. You may run ./bin/spark-sql --help for a complete list of all available options.

Working with other databases

We have seen how you can work with Hive, which is fast becoming a defacto data warehouse option in the open source community. However, most of the data in the enterprises beginning with Hadoop or Spark journey is to stored in traditional databases including Oracle, Teradata, Greenplum, and Netezza. Spark provides you with the option to access those data sources using JDBC, which returns results as DataFrames. For the sake of brevity, we'll only share the Scala example of connecting to a Teradata database. Please remember to copy your database's JDBC driver class to all nodes...