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 learned about the concept of ML and the different types of ML algorithms. You also learned about some of the real-world applications of ML to help businesses minimize losses and maximize revenues and accelerate their time to market. You were introduced to the necessity of scalable ML and two different techniques for scaling out ML algorithms. Apache Spark's native ML Library, MLlib, was introduced, along with its major components.

Finally, you learned a few techniques to perform data wrangling to clean, manipulate, and transform data to make it more suitable for the data science process. In the following chapter, you will learn about the send phase of the ML process, called feature extraction and feature engineering, where you will learn to apply various scalable algorithms to transform individual data fields to make them even more suitable for data science applications.