This book looks at how to tackle challenges commonly faced in different aspects of data engineering. Paul Crickard starts 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. He then looks at how to transform and clean data and perform analytics to get the most out of data.The book also covers how to work with big data of varying complexity and production databases, and build data pipelines.
Author: Paul Crickard
Date: October 2020
Audience: Python developers
- Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples
- Design data models and learn how to extract, transform, and load (ETL) data using Python
- Schedule, automate, and monitor complex data pipelines in production
- 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
For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.
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