Book Image

Essential PySpark for Scalable Data Analytics

By : Sreeram Nudurupati
Book Image

Essential PySpark for Scalable Data Analytics

By: Sreeram Nudurupati

Overview of this book

Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems.
Table of Contents (19 chapters)
1
Section 1: Data Engineering
6
Section 2: Data Science
13
Section 3: Data Analysis

Summary

In this chapter, you learned about SQL as a declarative language that has been universally accepted as the language for structured data analysis because of its ease of use and expressiveness. You learned about the basic constructions of SQL, including the DDL and DML dialects of SQL. You were introduced to the Spark SQL engine as the unified distributed query engine that powers both Spark SQL and DataFrame APIs. SQL optimizers, in general, were introduced, and Spark's very own query optimizer Catalyst was also presented, along with its inner workings as to how it takes a Spark SQL query and converts it into Java JVM bytecode. A reference to the Spark SQL language was also presented, along with the most important DDL and DML statements, with examples. Finally, a few performance optimizations techniques were also discussed to help you get the best out of Spark SQL for all your data analysis needs. In the next chapter, we will extend our Spark SQL knowledge and see how external...