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

Finalizing your data pipelines for production

In the last few chapters, you have learned about the features and methods for creating production data pipelines. There are still a few more features needed before you can deploy your data pipelines—backpressure, processor groups with input and output ports, and funnels. This section will walk you through each one of these features.


In your data pipelines, each processor or task will take different amounts of time to finish. For example, a database query may return hundreds of thousands of results that are split into single flowfiles in a few seconds, but the processor that evaluates and modifies the attributes within the flowfiles may take much longer. It doesn't make sense to dump all of the data into the queue faster than the downstream processor can actually process it. Apache NiFi allows you to control the number of flowfiles or the size of the data that is sent to the queue. This is called backpressure...