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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

By : Kukreja
4.7 (58)
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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

4.7 (58)
By: Kukreja

Overview of this book

In the world of ever-changing data and 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 know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.
Table of Contents (17 chapters)
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1
Section 1: Modern Data Engineering and Tools
5
Section 2: Data Pipelines and Stages of Data Engineering
11
Section 3: Data Engineering Challenges and Effective Deployment Strategies

Chapter 10: Solving Data Engineering Challenges

In the past few chapters, we learned about the data lakehouse architecture. After covering several exercises, we learned how a data engineer builds and deploys the bronze, silver, and gold layers of the lakehouse. Data in the lakehouse increases and changes over time. As new data sources get added and the previous ones undergo modifications, the data engineering practice needs to keep up with this growth. Just like anything else in the industry, the role of the data engineer needs to evolve as well. In addition to building and deploying data pipelines, they need to cover several other complicated aspects of data engineering that were not covered previously. They must learn to deal with these new challenges.

In this chapter, we will cover the following topics:

  • Schema evolution
  • Sharing data
  • Data governance

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