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Data Engineering with Python : Work with massive datasets to design data models and automate data pipelines using Python
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 challen...
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| Auteur principal : | |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Data Engineering with Python : Work with massive datasets to design data models and automate data pipelines using Python / Paul Crickard |
| Publié : |
Birmingham :
Packt Publishing
, 2020 |
| Description matérielle : | 1 vol. (XII-337 p.) |
| Sujets : | |
| Documents associés : | Autre format:
Data Engineering with Python |
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| 200 | 1 | |a Data Engineering with Python |e Work with massive datasets to design data models and automate data pipelines using Python |f Paul Crickard | |
| 214 | 0 | |a Birmingham |c Packt Publishing |d 2020 | |
| 215 | |a 1 vol. (XII-337 p.) |c couv. ill. en coul. |d 24 cm | ||
| 320 | |a Index | ||
| 330 | |a 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. |2 4e de couverture | ||
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