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

Chapter 10: Deploying Data Pipelines

In software engineering, you will usually have development, testing, and production environments. The testing environment may be called quality control or staging or some other name, but the idea is the same. You develop in an environment, then push it to another environment that will be a clone of the production environment and if everything goes well, then it is pushed into the production environment. The same methodology is used in data engineering. So far, you have built data pipelines and run them on a single machine. In this chapter, you will learn methods for building data pipelines that can be deployed to a production environment.

In this chapter, we're going to cover the following main topics:

  • Finalizing your data pipelines for production
  • Using the NiFi variable registry
  • Deploying your data pipelines