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

Preface

Apache Spark is a unified data analytics engine designed to process huge volumes of data in a fast and efficient way. PySpark is the Python language API of Apache Spark that provides 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 then begin your data analytics journey with the data engineering process, learning to perform data ingestion, data cleansing, and integration at scale.

This book will also help you build real-time analytics pipelines that enable you to gain insights much faster. Techniques for building cloud-based data lakes are presented along with Delta Lake, which brings reliability and performance to data lakes.

A newly emerging paradigm called the Data Lakehouse is presented, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. You'll learn how to perform scalable data science and machine learning using PySpark, including data preparation, feature engineering, model training, and model productionization techniques. Techniques to scale out standard Python machine learning libraries are also presented, along with a new pandas-like API on top of PySpark called Koalas.