Nouveauté
Fundamentals of Python Data Engineering
Par :Formats :
Actuellement indisponible
Cet article est actuellement indisponible, il ne peut pas être commandé sur notre site pour le moment. Nous vous invitons à vous inscrire à l'alerte disponibilité, vous recevrez un e-mail dès que cet ouvrage sera à nouveau disponible.
Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format ePub est :
- Compatible avec une lecture sur My Vivlio (smartphone, tablette, ordinateur)
- Compatible avec une lecture sur liseuses Vivlio
- Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony
, qui est-ce ?Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement
Pour en savoir plus sur nos ebooks, consultez notre aide en ligne ici
C'est si simple ! Lisez votre ebook avec l'app Vivlio sur votre tablette, mobile ou ordinateur :
- FormatePub
- ISBN8230510840
- EAN9798230510840
- Date de parution17/05/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIndependently Published
Résumé
"Fundamentals of Python Data Engineering: Architecting Resilient Data Systems for the Enterprise" is a comprehensive guide for data professionals looking to master Python-based data engineering. This book bridges theoretical concepts with practical implementation, covering the complete data engineering lifecycle from source systems to data serving. Readers will learn how to design scalable architectures, implement robust data pipelines, and build enterprise-grade data systems using Python's powerful ecosystem.
The book explores essential topics including data storage solutions, transformation techniques, orchestration frameworks, and security best practices. It provides hands-on examples for working with various data sources, implementing ETL/ELT processes, and creating production-ready data systems. Written for both aspiring and experienced data engineers, this book emphasizes real-world applications while covering advanced topics like distributed computing, cloud-native architectures, and DataOps methodologies.
Each chapter builds on fundamental concepts while introducing modern tools and techniques used in industry-leading organizations. By the end of this book, readers will have gained the knowledge and skills to architect, build, and maintain resilient Python-based data systems that can handle enterprise-scale challenges while meeting business requirements.
The book explores essential topics including data storage solutions, transformation techniques, orchestration frameworks, and security best practices. It provides hands-on examples for working with various data sources, implementing ETL/ELT processes, and creating production-ready data systems. Written for both aspiring and experienced data engineers, this book emphasizes real-world applications while covering advanced topics like distributed computing, cloud-native architectures, and DataOps methodologies.
Each chapter builds on fundamental concepts while introducing modern tools and techniques used in industry-leading organizations. By the end of this book, readers will have gained the knowledge and skills to architect, build, and maintain resilient Python-based data systems that can handle enterprise-scale challenges while meeting business requirements.
"Fundamentals of Python Data Engineering: Architecting Resilient Data Systems for the Enterprise" is a comprehensive guide for data professionals looking to master Python-based data engineering. This book bridges theoretical concepts with practical implementation, covering the complete data engineering lifecycle from source systems to data serving. Readers will learn how to design scalable architectures, implement robust data pipelines, and build enterprise-grade data systems using Python's powerful ecosystem.
The book explores essential topics including data storage solutions, transformation techniques, orchestration frameworks, and security best practices. It provides hands-on examples for working with various data sources, implementing ETL/ELT processes, and creating production-ready data systems. Written for both aspiring and experienced data engineers, this book emphasizes real-world applications while covering advanced topics like distributed computing, cloud-native architectures, and DataOps methodologies.
Each chapter builds on fundamental concepts while introducing modern tools and techniques used in industry-leading organizations. By the end of this book, readers will have gained the knowledge and skills to architect, build, and maintain resilient Python-based data systems that can handle enterprise-scale challenges while meeting business requirements.
The book explores essential topics including data storage solutions, transformation techniques, orchestration frameworks, and security best practices. It provides hands-on examples for working with various data sources, implementing ETL/ELT processes, and creating production-ready data systems. Written for both aspiring and experienced data engineers, this book emphasizes real-world applications while covering advanced topics like distributed computing, cloud-native architectures, and DataOps methodologies.
Each chapter builds on fundamental concepts while introducing modern tools and techniques used in industry-leading organizations. By the end of this book, readers will have gained the knowledge and skills to architect, build, and maintain resilient Python-based data systems that can handle enterprise-scale challenges while meeting business requirements.