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 have learned how to use the NiFi GUI to monitor your data pipelines using the status bar, the bulletin, and counters. You also learned how to add processors that can send information to you inside your data pipeline. With the PutSlack processor, you were able to send yourself direct messages when there was a failure, and you passed data from the flowfile in the message with the NiFi expression language. Lastly, you learned how to use the API to write your own monitoring tools and grab the same data as is in the NiFi GUI—even reading the contents of a single flowfile.

In the next chapter, you will learn how to deploy your production pipelines. You will learn how to use processor groups, templates, versions, and variables to allow you to import data pipelines to a production NiFi instance with minimal configuration.