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

Deploying your data pipelines

There are many ways to handle the different environments—development, testing, production—and how you choose to do that is up to what works best with your business practices. Having said that, any strategy you take should involve using the NiFi registry.

Using the simplest strategy

The simplest strategy would be to run NiFi over the network and split the canvas into multiple environments. When you have promoted a process group, you would move it in to the next environment. When you needed to rebuild a data pipeline, you would add it back to development and modify it, then update the production data pipeline to the newest version. Your NiFi instance would look like the following screenshot:

Figure 10.11 – A single NiFi instance working as DEV, TEST, and PROD

Notice in the preceding screenshot that only PROD has a green checkmark. The DEV environment created the processor group, then changes were committed...