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 were introduced to the need for real-time data analytics systems and the advantages they have to offer in terms of getting the freshest data to business users, helping businesses improve their time to market, and minimizing any lost opportunity costs. The architecture of a typical real-time analytics system was presented, and the major components were described. A real-time analytics architecture using Apache Spark's Structured Streaming was also depicted. A few examples of prominent industry use cases of real-time data analytics were described. Also, you were introduced to a simplified Lambda Architecture using the combination of Structured Streaming and Delta Lake. The use case for CDC, including its requirements and benefits, was presented, and techniques for ingesting CDC data into Delta Lake were presented along with working examples leveraging Structured Streaming for implementing a CDC use case.

Finally, you learned a technique for progressively...