Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Architecture


Remember that, although Spark is used for the speed of its in-memory distributed processing, it doesn't provide storage. You can use the Host (local) filesystem to read and write your data, but if your data volumes are big enough to be described as big data, then it makes sense to use a cloud-based distributed storage system such as OpenStack Swift Object Storage, which can be found in many cloud environments and can also be installed in private data centers.

Note

In case very high I/O is needed, HDFS would also be an option. More information on HDFS can be found here: http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html.

The development environment

The Scala language will be used for the coding samples in this book. This is because, as a scripting language, it produces less code than Java. It can also be used from the Spark shell as well as compiled with Apache Spark applications. We will be using the sbt tool to compile the Scala code, which we...