Book Image

Data Engineering with Python

By : Paul Crickard
Book Image

Data Engineering with Python

By: Paul Crickard

Overview of this book

Data engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.
Table of Contents (21 chapters)
Section 1: Building Data Pipelines – Extract Transform, and Load
Section 2:Deploying Data Pipelines in Production
Section 3:Beyond Batch – Building Real-Time Data Pipelines


In this chapter, you learned how to build and deploy a production data pipeline. You learned how to create TEST and PRODUCTION environments and built the data pipeline in TEST. You used the filesystem as a sample data lake and learned how you would read files from the lake and monitor them as they were processed. Instead of loading data into the data warehouse, this chapter taught you how to use a staging database to hold the data so that it could be validated before being loaded into the data warehouse. Using Great Expectations, you were able to build a validation processor group that would scan the staging database to determine whether the data was ready to be loaded into the data warehouse. Lastly, you learned how to deploy the data pipeline into PRODUCTION. With these skills, you can now fully build, test, and deploy production batch data pipelines.

In the next chapter, you will learn how to build Apache Kafka clusters. Using Kafka, you will begin to learn how to process...