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

ML overview

Machine Learning is a field of AI and computer science that leverages statistical models and computer science algorithms for learning patterns inherent in data, without being explicitly programmed. ML consists of algorithms that automatically convert patterns within data into models. Where pure mathematical or rule-based models perform the same task over and over again, an ML model learns from data and its performance can be greatly improved by exposing it to vast amounts of data.

A typical ML process involves applying an ML algorithm to a known dataset called the training dataset, to generate a new ML model. This process is generally termed model training or model fitting. Some ML models are trained on datasets containing a known correct answer that we intend to predict in an unknown dataset. This known, correct value in the training dataset is termed the label.

Once the model is trained, the resultant model is applied to new data in order to 
predict the...