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

Transforming raw data into enriched meaningful data

Every data analytics system consists of a few key stages, including data ingestion, data transformation, and loading into a data warehouse or a data lake. Only after the data passes through these stages does it become ready for consumption by end users for descriptive and predictive analytics. There are two common industry practices for undertaking this process, widely known as Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT). In this section, you will explore both these methods of data processing and understand their key differences. You will also learn about the key advantages ELT has to offer over ETL in the context of big data analytics in the cloud.

Extracting, transforming, and loading data

This is the typical data processing methodology that's followed by almost all data warehousing systems. In this methodology, data is extracted from the source systems and stored in a temporary storage location...