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

Real-world supervised learning applications

In the past, data science and machine learning were used exclusively for academic research purposes. However, over the past decade, this field has found its use in actual business applications to help businesses find their competitive edge, improve overall business performance, and become profitable. In this section, we will look at some real-world applications of machine learning.

Regression applications

Some of the applications of machine learning regression models and how they help improve business performance will be presented in this section.

Customer lifetime value estimation

In any retail or CPG kind of business where customer churn is a huge factor, it is necessary to direct marketing spend at those customers who are profitable. In non-subscription kinds of businesses, typically 20% of the customer base generates up to 80% of revenue. Machine learning models can be leveraged to model and predict each customer's lifetime...