Data Engineering on Azure
Σεπ 2021 · Πωλήθηκε από Simon and Schuster
ebook
336
Σελίδες
family_home
Κατάλληλο
info
Σχετικά με το 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
Ανακαλύψτε περισσότερα
Σχετικά με τον συγγραφέα
Vlad Riscutia is a software architect at Microsoft.
Αξιολογήστε αυτό το ebook
Πείτε μας τη γνώμη σας.
Πληροφορίες ανάγνωσης
Smartphone και tablet
Εγκαταστήστε την εφαρμογή Βιβλία Google Play για Android και iPad/iPhone. Συγχρονίζεται αυτόματα με τον λογαριασμό σας και σας επιτρέπει να διαβάζετε στο διαδίκτυο ή εκτός σύνδεσης, όπου κι αν βρίσκεστε.
Φορητοί και επιτραπέζιοι υπολογιστές
Μπορείτε να ακούσετε ηχητικά βιβλία τα οποία αγοράσατε στο Google Play, χρησιμοποιώντας το πρόγραμμα περιήγησης στον ιστό του υπολογιστή σας.
eReader και άλλες συσκευές
Για να διαβάσετε περιεχόμενο σε συσκευές e-ink, όπως είναι οι συσκευές Kobo eReader, θα χρειαστεί να κατεβάσετε ένα αρχείο και να το μεταφέρετε στη συσκευή σας. Ακολουθήστε τις αναλυτικές οδηγίες του Κέντρου βοήθειας για να μεταφέρετε αρχεία σε υποστηριζόμενα eReader.
