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

Summary

In this chapter, you were introduced to the end-to-end ML life cycle and the various steps involved in it. MLflow is a complete, end-to-end ML life cycle management tool. The MLflow Tracking component was presented, which is useful for streaming the ML experimentation process and helps you track all its attributes, including the data version, ML code, model parameters and metrics, and any other arbitrary artifacts. MLflow Model was introduced as a standards-based model format that provides model portability and reproducibility. MLflow Model Registry was also explored, which is a central model repository that supports the entire life cycle of a newly created model, from staging to production to archival. Model serving mechanisms, such as using batch and online processes, were also introduced. Finally, continuous delivery for ML was introduced. It is used to streamline the entire ML life cycle and automate the model life cycle using Model Registry features, such as the ability...