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

Chapter 7: Features of a Production Pipeline

In this chapter, you will learn several features that make a data pipeline ready for production. You will learn about building data pipelines that can be run multiple times without changing the results (idempotent). You will also learn what to do if transactions fail (atomicity). And you will learn about validating data in a staging environment. This chapter will use a sample data pipeline that I currently run in production.

For me, this pipeline is a bonus, and I am not concerned with errors, or missing data. Because of this, there are elements missing in this pipeline that should be present in a mission critical, or production, pipeline. Every data pipeline will have different acceptable rates of errors – missing data – but in production, your pipelines should have some extra features that you have yet to learn.

In this chapter, we're going to cover the following main topics:

  • Staging and validating data...