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

Data wrangling with Apache Spark and MLlib

Data wrangling, also referred to within the data science community as data munging, or simply data preparation, is the first step in a typical data science process. Data wrangling involves sampling, exploring, selecting, manipulating, and cleansing data to make it ready for ML applications. Data wrangling takes up to 60 to 80 percent of the whole data science process and is the most crucial step in guaranteeing the accuracy of the ML model being built. The following sections explore the data wrangling process using Apache Spark and MLlib.

Data preprocessing

Data preprocessing is the first step in the data wrangling process and involves gathering, exploring, and selecting the data elements useful for solving the problem at hand. The data science process typically succeeds the data engineering process and the assumption here is that clean and integrated data is already available in the data lake. However, data that is clean enough for...