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)
1
Section 1: Building Data Pipelines – Extract Transform, and Load
8
Section 2:Deploying Data Pipelines in Production
14
Section 3:Beyond Batch – Building Real-Time Data Pipelines

Summary

In this chapter, you learned how to perform basic EDA with an eye toward finding errors or problems within your data. You then learned how to clean your data and fix common data issues. With this set of skills, you built a data pipeline in Apache Airflow.

In the next chapter, you will walk through a project, building a 311 data pipeline and dashboard in Kibana. This project will utilize all of the skills you have acquired up to this point and will introduce a number of new skills – such as building dashboards and making API calls.