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

Using Python with the NiFi REST API

Using Python and the NiFi REST API, you could write your own monitoring tools, or wire up a dashboard. The NiFi REST API documentation is located at You can see all of the different endpoints by type and some information about each of them. This section will highlight some of the endpoints that you have covered in this chapter but by using the GUI.

The first thing we can look at are the system diagnostics. System diagnostics will show you your resource usage. You can see heap size, threads, repository usage, and several other metrics. To call the endpoint with requests, you can use the following code:

#'512 MB'