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Data Engineering with Python

Data Engineering with Python

By : Paul Crickard
2.6 (24)
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Data Engineering with Python

Data Engineering with Python

2.6 (24)
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)
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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

Performing exploratory data analysis in Python

Before you can clean your data, you need to know what your data looks like. As a data engineer, you are not the domain expert and are not the end user of the data, but you should know what the data will be used for and what valid data would look like. For example, you do not need to be a demographer to know that an age field should not be negative, and the frequency of values over 100 should be low.

Downloading the data

In this chapter, you will use real e-scooter data from the City of Albuquerque. The data contains trips taken using e-scooters from May to July 22, 2019. You will need to download the e-scooter data from https://github.com/PaulCrickard/escooter/blob/master/scooter.csv. The repository also contains the original Excel file as well as some other summary files provided by the City of Albuquerque.

Basic data exploration

Before you can clean your data, you have to know what your data looks like. The process of understanding...

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Data Engineering with Python
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