Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data

Key Features:

• Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platforms
• Learn how to ingest, process, and analyze data that can be later used for training machine learning models
• Understand how to operationalize data models in production using curated data

Book Description:

In the world of ever-changing data and ever-evolving schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on.

Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way.

By the end of this data engineering book, you'll have learned how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and a