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 saw the challenges that are faced by data warehouses and data lakes in designing and implementing large-scale data processing systems that deal with large-scale data. We also looked at the need for businesses to move from advanced analytics to simple descriptive analytics and how the existing systems cannot solve both problems simultaneously. Then, the data lakehouse paradigm was introduced, which solves the challenges of both data warehouses and data lakes and how it bridges the gap of both systems by combining the best elements from both. The reference architecture for data lakehouses was presented and a few data lakehouse candidates were presented from existing commercially available, large-scale data processing systems, along with their drawbacks. Next, an Apache Spark-based data lakehouse architecture was presented that made use of the Delta Lake and cloud-based data lakes. Finally, some advantages of data lakehouses were presented, along with a few...