Book Image

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

By : Manoj Kukreja
5 (2)
Book Image

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

5 (2)
By: Manoj 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)
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

The deployment strategy

We have worked extremely hard to get to the point where we have three functional pipelines, as follows:

  • The Electroniz ingestion pipeline: electroniz_batch_ingestion_pipeline
  • The Electroniz curation pipeline: electroniz_curation_pipeline
  • The Electroniz aggregation pipeline: electroniz_aggregation_pipeline

Just as a recap, in the last few chapters, we followed multiple steps in order to create these pipelines. After their creation, we invoked each one manually to unit test their functionality. Finally, we validated the data that each one produced to make sure it matched the expectation of the Electroniz use cases. That's a lot of work, so we should be proud to have reached this far.

Assuming we are happy with the outcomes of the unit tests performed on the preceding pipelines, it is time to start thinking about the best way to deploy these pipelines in production. As per best practices, the three pipelines should run as one complete...