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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

Understanding data consumption

Before we start verifying the aggregated data, we should focus on how our end users will be able to consume data for dashboarding, ML, and AI purposes. As per the laid-out architecture of the Electroniz lakehouse, we decided to publish data from both the gold and silver layers.

Publishing data from the gold layer is necessary; otherwise, how would users be able to access aggregated data? But why do we need to publish data from the silver layer? You guessed it – analytics is an ongoing process. In the future, users may want to create new dashboards and ML models for the betterment of the company.

Important

Publishing data from the gold and silver layers is acceptable because they store data that is in a clean and secure state. But the same cannot be said for data in the bronze layer. Publishing raw/unclean data not only throws a lot of work around standardization, validation, and deduplication at end users, but it also ends up exposing...

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