Data Engineering on Azure
sept. 2021 · Vendu par Simon and Schuster
Ebook
336
Pages
family_home
Admissible
info
À propos de cet ebook
Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure.
Summary
In Data Engineering on Azure you will learn how to:
Pick the right Azure services for different data scenarios
Manage data inventory
Implement production quality data modeling, analytics, and machine learning workloads
Handle data governance
Using DevOps to increase reliability
Ingesting, storing, and distributing data
Apply best practices for compliance and access control
Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify.
About the book
In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms.
What's inside
Data inventory and data governance
Assure data quality, compliance, and distribution
Build automated pipelines to increase reliability
Ingest, store, and distribute data
Production-quality data modeling, analytics, and machine learning
About the reader
For data engineers familiar with cloud computing and DevOps.
About the author
Vlad Riscutia is a software architect at Microsoft.
Table of Contents
1 Introduction
PART 1 INFRASTRUCTURE
2 Storage
3 DevOps
4 Orchestration
PART 2 WORKLOADS
5 Processing
6 Analytics
7 Machine learning
PART 3 GOVERNANCE
8 Metadata
9 Data quality
10 Compliance
11 Distributing data
Summary
In Data Engineering on Azure you will learn how to:
Pick the right Azure services for different data scenarios
Manage data inventory
Implement production quality data modeling, analytics, and machine learning workloads
Handle data governance
Using DevOps to increase reliability
Ingesting, storing, and distributing data
Apply best practices for compliance and access control
Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify.
About the book
In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms.
What's inside
Data inventory and data governance
Assure data quality, compliance, and distribution
Build automated pipelines to increase reliability
Ingest, store, and distribute data
Production-quality data modeling, analytics, and machine learning
About the reader
For data engineers familiar with cloud computing and DevOps.
About the author
Vlad Riscutia is a software architect at Microsoft.
Table of Contents
1 Introduction
PART 1 INFRASTRUCTURE
2 Storage
3 DevOps
4 Orchestration
PART 2 WORKLOADS
5 Processing
6 Analytics
7 Machine learning
PART 3 GOVERNANCE
8 Metadata
9 Data quality
10 Compliance
11 Distributing data
En découvrir plus
Quelques mots sur l'auteur
Vlad Riscutia is a software architect at Microsoft.
Attribuez une note à ce ebook
Faites-nous part de votre avis.
Informations sur la lecture
Téléphones intelligents et tablettes
Installez l'appli Google Play Livres pour Android et iPad ou iPhone. Elle se synchronise automatiquement avec votre compte et vous permet de lire des livres en ligne ou hors connexion, où que vous soyez.
Ordinateurs portables et de bureau
Vous pouvez écouter les livres audio achetés sur Google Play en utilisant le navigateur Web de votre ordinateur.
Liseuses et autres appareils
Pour pouvoir lire des ouvrages sur des appareils utilisant la technologie e-Ink, comme les liseuses électroniques Kobo, vous devez télécharger un fichier et le transférer sur l'appareil en question. Suivez les instructions détaillées du centre d'aide pour transférer les fichiers sur les liseuses électroniques compatibles.
