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 13: Integrating External Tools with Spark SQL

Business intelligence (BI) refers to the capabilities that enable organizations to make informed, data-driven decisions. BI is a combination of data processing capabilities, data visualizations, business analytics, and a set of best practices that enable, refine, and streamline organizations' business processes by helping them in both strategic and tactical decision making. Organizations typically rely on specialist software called BI tools for their BI needs. BI tools combine strategy and technology to gather, analyze, and interpret data from various sources and provide business analytics about the past and present state of a business.

BI tools have traditionally relied on data warehouses as data sources and data processing engines. However, with the advent of big data and real-time data, BI tools have branched out to using data lakes and other new data storage and processing technologies as data sources. In this chapter...