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 4: Real-Time Data Analytics

In the modern big data world, data is being generated at a tremendous pace, that is, faster than any of the past decade's technologies can handle, such as batch processing ETL tools, data warehouses, or business analytics systems. It is essential to process data and draw insights in real time for businesses to make tactical decisions that help them to stay competitive. Therefore, there is a need for real-time analytics systems that can process data in real or near real-time and help end users get to the latest data as quickly as possible.

In this chapter, you will explore the architecture and components of a real-time big data analytics processing system, including message queues as data sources, Delta as the data sink, and Spark's Structured Streaming as the stream processing engine. You will learn techniques to handle late-arriving data using stateful processing Structured Streaming. The techniques for maintaining an exact replica...