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 (12 chapters)

What is ETL?


ELT stands for Extraction, Transformation,and Loading. The term has been around for decades and it represents an industry standard representing the data movement and transformation process to build data pipelines to deliver BI and Analytics. ETL processes are widely used on the data migration and master data management initiatives. Since the focus of our book is on Spark, we'll lightly touch upon the subject of ETL, but will not go into more detail.

Exaction

Extraction is the first part of the ETL process representing the extraction of data from source systems. This is often one of the most important parts of the ETL process, and it sets the stage for further downstream processing. There are a few major things to consider during an extraction process:

  • The source system type (RDBMS, NoSQL, FlatFiles, Twitter/Facebook streams)
  • The file formats (CSV, JSON, XML, Parquet, Sequence, Object files)
  • The frequency of the extract ( Daily, Hourly, Every second)
  • The size of the extract...