Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
Table of Contents (15 chapters)

Saving Data in the Correct Format

In the previous chapters, we were focusing on processing and loading data. We learned about transformations, actions, joining, shuffling, and other aspects of Spark.

In this chapter, we will learn how to save data in the correct format and also save data in plain text format using Spark's standard API. We will also leverage JSON as a data format, and learn how to use standard APIs to save JSON. Spark has a CSV format and we will leverage that format as well. We will then learn more advanced schema-based formats, where support is required to import third-party dependencies. Following that, we will use Avro with Spark and learn how to use and save the data in a columnar format known as Parquet. By the end of this chapter, we will have also learned how to retrieve data to validate whether it is stored in the proper way.

In this chapter, we will...