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

The machine learning process

A typical data analytics and data science process involves gathering raw data, cleaning data, consolidating data, and integrating data. Following this, we apply statistical and machine learning techniques to the preprocessed data in order to generate a machine learning model and, finally, summarize and communicate the results of the process to business stakeholders in the form of data products. A high-level overview of the machine learning process is presented in the following diagram:

Figure 6.1 – The data analytics and data science process

As you can see from the preceding diagram, the actual machine learning process itself is just a small portion of the entire data analytics process. Data teams spend a good amount of time curating and preprocessing data, and just a portion of that time is devoted to building actual machine learning models.

The actual machine learning process involves stages that allow you to carry out...