- Big Data Analytics
- Table of Contents
- Big Data Analytics
- Credits
- About the Author
- Acknowledgement
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Big Data Analytics at a 10,000-Foot View
- 2. Getting Started with Apache Hadoop and Apache Spark
- 3. Deep Dive into Apache Spark
- Starting Spark daemons
- Learning Spark core concepts
- Lifecycle of Spark program
- Spark applications
- Persistence and caching
- Spark resource managers – Standalone, YARN, and Mesos
- Summary
- 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets
- History of Spark SQL
- Architecture of Spark SQL
- Introducing SQL, Datasources, DataFrame, and Dataset APIs
- Evolution of DataFrames and Datasets
- Why Datasets and DataFrames?
- When to use RDDs, Datasets, and DataFrames?
- Analytics with DataFrames
- Analytics with the Dataset API
- Data Sources API
- Spark SQL as a distributed SQL engine
- Hive on Spark
- Summary
- 5. Real-Time Analytics with Spark Streaming and Structured Streaming
- 6. Notebooks and Dataflows with Spark and Hadoop
- 7. Machine Learning with Spark and Hadoop
- 8. Building Recommendation Systems with Spark and Mahout
- 9. Graph Analytics with GraphX
- 10. Interactive Analytics with SparkR
- Index
Practical exercises in this book are demonstrated on virtual machines (VM) from Cloudera, Hortonworks, MapR, or prebuilt Spark for Hadoop for getting started easily. The same exercises can be run on a bigger cluster as well.
Prerequisites for using virtual machines on your laptop:
- RAM: 8 GB and above
- CPU: At least two virtual CPUs
- The latest VMWare player or Oracle VirtualBox must be installed for Windows or Linux OS
- Latest Oracle VirtualBox, or VMWare Fusion for Mac
- Virtualization enabled in BIOS
- Browser: Chrome 25+, IE 9+, Safari 6+, or Firefox 18+ recommended (HDP Sandbox will not run on IE 10)
- Putty
- WinScP
The Python and Scala programming languages are used in chapters, with more focus on Python. It is assumed that readers have a basic programming background in Java, Scala, Python, SQL, or R, with basic Linux experience. Working experience within Big Data environments on Hadoop platforms would provide a quick jump start for building Spark applications.
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