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 cloud resources and deploying 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 artificial intelligence (AI) tasks.
Who this book is for ?
- This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Section 1: Modern Data Engineering and Tools
- This section introduces you to the world of data engineering. It gives you an understanding of data engineering concepts and architectures. Furthermore, it educates you on how to effectively utilize the Microsoft Azure cloud services for data engineering.
This section contains the following chapters: • Chapter 1, The Story of Data Engineering and Analytics • Chapter 2, Discovering Storage and Compute Data Lake Architectures • Chapter 3, Data Engineering on Microsoft Azure
Section 2: Data Pipelines and Stages of Data Engineering
- This section provides you with advanced knowledge about data pipelines and various stages of data engineering. We will focus our learning on the Lakehouse architecture.
This section contains the following chapters: • Chapter 4, Understanding Data Pipelines • Chapter 5, Data Collection Stage – The Bronze Layer • Chapter 6, Understanding Delta Lake • Chapter 7, Data Curation Stage – The Silver Layer • Chapter 8, Data Aggregation Stage – The Gold Layer
Section 3: Data Engineering Challenges and Effective Deployment Strategies
This part of the book educates you on the challenges faced by data engineering professionals. The concepts of Delta Lake are introduced. Lastly, a number of data engineering challenges will be highlighted with real-life examples of how Delta Lake can help deal with these issues effectively.
This section also educates you on effective deployment strategies for a data lake. Once the individual code components dealing with the various stages of data engineering have been built, these strategies play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous fashion.
This section contains the following chapters: • Chapter 9, Deploying and Monitoring Pipelines in Production • Chapter 10, Solving Data Engineering Challenges • Chapter 11, Infrastructure Provisioning • Chapter 12, Continuous Integration and Deployment (CI/CD) of Data Pipelines
Conclusion
- I thoroughly enjoyed reading "Data Engineering with Apache Spark, Delta Lake, and Lakehouse" by Manoj Kukreja. Thank you to Shifa Ansari at Packt team for sharing this book and for the opportunity of an Editorial Review of the same. Strongly recommend it if you’re Learning to build Data Engineering Pipelines with Apache Spark, Delta Lake and Lakehouse.
Personally, I loved 👀👇:
👉 The book starts by exploring the challenges you may face in the data engineering world. It also explores the architectural and design patterns for building efficient data ingestion pipelines.
👉 the book does a good job of explaining how to Add ACID transactions to Apache Spark using Delta Lake and it helps you Understand effective design strategies to build enterprise-grade data lakes
👉 the book Orchestrates a data pipeline for preprocessing data using Apache Spark and Delta Lake APIs and goes through various options for Automating deployment and monitoring of data pipelines in production.
👉 Finally it helps you get to grips with securing, monitoring, and managing data pipelines models efficiently.
👉 Link of the Book is here
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