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

Chapter 8: Unsupervised Machine Learning

In the previous two chapters, you were introduced to the supervised learning class of machine learning algorithms, their real-world applications, and how to implement them at scale using Spark MLlib. In this chapter, you will be introduced to the unsupervised learning category of machine learning, where you will learn about parametric and non-parametric unsupervised algorithms. A few real-world applications of clustering and association algorithms will be presented to help you understand the applications of unsupervised learning to solve real-life problems. You will gain basic knowledge and understanding of clustering and association problems when using unsupervised machine learning. We will also look at the implementation details of a few clustering algorithms in Spark ML, such as K-means clustering, hierarchical clustering, latent Dirichlet allocation, and an association algorithm called alternating least squares.

In this chapter, we&apos...