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 to install and configure many of the tools used by data engineers. Having done so, you now have a working environment in which you can build data pipelines. In production, you would not run all these tools on a single machine, but for the next few chapters, this will help you learn and get started quickly. You now have two working databases – Elasticsearch and PostgreSQL – as well as two tools for building data pipelines – Apache NiFi and Apache Airflow.

In the next chapter, you will start to use Apache NiFi and Apache Airflow (Python) to connect to files, as well as Elasticsearch and PostgreSQL. You will build your first pipeline in NiFi and Airflow to move a CSV to a database.