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 use Python to query and insert data into both relational and NoSQL databases. You also learned how to use both Airflow and NiFi to create data pipelines. Database skills are some of the most important for a data engineer. There will be very few data pipelines that do not touch on them in some way. The skills you learned in this chapter provide the foundation for the other skills you will need to learn – primarily SQL. Combining strong SQL skills with the data pipeline skills you learned in this chapter will allow you to accomplish most of the data engineering tasks you will encounter.

In the examples, the data pipelines were not idempotent. Every time they ran, you got new results, and results you did not want. We will fix that in Section 2, Deploying Pipelines into Production. But before you get to that, you will need to learn how to handle common data issues, and how to enrich and transform your data.

The next chapter will...