Essential PySpark for Scalable Data Analytics

Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale

Key Features

• Discover how to convert huge amounts of raw data into meaningful and actionable insights
• Use Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analytics
• Perform data ingestion, cleansing, and integration for ML, data analytics, and data visualization

Book Description

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.

What you will learn

• Understand the role of distributed computing in the world of big data
• Gain an appreciation for Apache Spark as the de facto go-to for big data processing
• Scale out your data analytics process using Apache Spark
• Build data pipelines using data lakes, and perform data visualization with PySpark and Spark SQL
• Leverage the cloud to build truly scalable and real-time data analytics applications
• Explore the applications of data science and scalable machine learning with PySpark
• Integrate your clean and curated data with BI and SQL analysis tools

Who this book is for

This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.

Table of Contents

1. Distributed Computing Primer
2. Data Ingestion
3. Data Cleansing and Integration
4. Real-time Data Analytics
5. Scalable Machine Learning with PySpark
6. Feature Engineering – Extraction, Transformation, and Selection
7. Supervised Machine Learning
8. Unsupervised Machine Learning
9. Machine Learning Life Cycle Management
10. Scaling Out Single-Node Machine Learning Using PySpark
11. Data Visualization with PySpark
12. Spark SQL Primer
13. Integrating External Tools with Spark SQL
14. The Data Lakehouse

Sreeram Nudurupati is a data analytics professional with years of experience in designing and optimizing data analytics pipelines at scale. He has a history of helping enterprises, as well as digital natives, build optimized analytics pipelines by using the knowledge of the organization, infrastructure environment, and current technologies.