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

Staging and validating data

When building production data pipelines, staging and validating data become extremely important. While you have seen basic data validation and cleaning in Chapter 5, Cleaning, Transforming, and Enriching Data, in production, you will need a more formal and automated way of performing these tasks. The next two sections will walk you through how to accomplish staging and validating data in production.

Staging data

In the NiFi data pipeline examples, data was extracted, and then passed along a series of connected processors. These processors performed some tasks on the data and sent the results to the next processor. But what happens if a processor fails? Do you start all over from the beginning? Depending on the source data, that may be impossible. This is where staging comes in to play. We will divide staging in to two different types: the staging of files or database dumps, and the staging of data in a database that is ready to be loaded into a warehouse...