Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python
by: Paul Crickard
Release Finelybook 出版日期： 2020
Publisher Finelybook 出版社： Packt Publishing
ISBN-13 书号： 9781839214189
ISBN-10 书号： 183921418X
Build,monitor,and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects
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.
What you will learn
Understand how data engineering supports data science workflows
Discover how to extract data from files and databases and then clean,transform,and enrich it
Configure processors for handling different file formats as well as both relational and NoSQL databases
Find out how to implement a data pipeline and dashboard to visualize results
Use staging and validation to check data before landing in the warehouse
Build real-time pipelines with staging areas that perform validation and handle failures
Get to grips with deploying pipelines in the production environment