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 a data pipeline using data from a REST API. You also added a flow to the data pipeline to allow you to backfill the data, or to recreate a database with all of the data using a single pipeline.

The second half of the chapter provided a basic overview of how to build a dashboard using Kibana. Dashboards will usually be outside the responsibilities of a data engineer. In smaller firms, however, this could very well be your job. Furthermore, being able to quickly build a dashboard can help validate your data pipeline and look for any possible errors in the data.

In the next chapter, we begin a new section of this book, where you will take the skills you have learned and improve them by making your pipelines ready for production. You will learn about deployment, better validation techniques, and other skills needed when you are running pipelines in a production environment.