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 applications of unsupervised learning

Unsupervised learning algorithms are being used today to solve some real-world business challenges. We will take a look at a few such challenges in this section.

Clustering applications

This section presents some of the real-world business applications of clustering algorithms.

Customer segmentation

Retail marketing teams, as well as business-to-customer organizations, are always trying to optimize their marketing spends. Marketing teams in particular are concerned with one specific metric called cost per acquisition (CPA). CPA is indicative of the amount that an organization needs to spend to acquire a single customer, and an optimal CPA means a better return on marketing investments. The best way to optimize CPA is via customer segmentation as this improves the effectiveness of marketing campaigns. Traditional customer segmentation takes standard customer features such as demographic, geographic, and social information...