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 learned how MiNiFi provides a means by which you can stream data to a NiFi instance. With MiNiFi, you can capture data from sensors, smaller devices such as a Raspberry Pi, or on regular servers where the data lives, without needing a full NiFi install. You learned how to set up and configure a remote processor group that allows you to talk to a remote NiFi instance.

In the Appendix, you will learn how you can cluster NiFi to run your data pipelines on different machines so that you can further distribute the load. This will allow you to reserve servers for specific tasks, or to spread large amounts of data horizontally across the cluster. By combining NiFi, Kafka, and Spark into clusters, you will be able to process more data than any single machine.