Developing Cloud Native Applications in Azure using .NET Core

Name: Developing Cloud Native Applications in Azure using .NET Core | A Practitioner's Guide to Design, Develop and Deploy Apps
Brand: De Gruyter
Price: 14.49 EUR
Availability: OnlineOnly

A Practitioner's Guide to Design, Develop and Deploy Apps

Rekha Kodali Dr. Gopala Krishna Behara Sankara Narayanan Govindarajulu(Autor*in)

De Gruyter (Verlag)

1. Auflage

Erschienen am 14. Februar 2020

322 Seiten

E-Book

ePUB mit Adobe-DRM

Systemvoraussetzungen

978-93-89328-75-2 (ISBN)

14,49 €inkl. 7% MwSt.

Systemvoraussetzungen

für ePUB mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Inhalt

Cover Page
Title Page
Copyright Page
Dedication
About the Authors
About the Reviewer
Acknowledgement
Preface
Errata
Table of Contents
1. An Introduction to Cloud Native Applications
Structure
Objective
Basics of Azure cloud-native Applications
Cloud-native applications: Microservices -principles
Cloud-native Applications - Design Patterns
Availability
Data management
Design and implementation:
Messaging
Management and monitoring
Performance and scalability
Resiliency
Security
Azure Components that Enable Cloud-native Application Development
Cloud Native Applications - Microservices in Azure
Service Fabric
Azure Kubernetes Service
Azure Functions
API Management
Cloud Native Applications - Enterprise Use Cases
Conclusion
Questions
2. Cloud Native Application Patterns
Structure
Objective
Challenges in Cloud development
Availability
Data management
Design and implementation
Messaging
Management and monitoring
Performance and scalability
Resiliency
Security
Factors applied to Cloud Native application adoption
Codebase
Dependencies
Config
Backing services
Build, release, run
Processes
Port binding
Concurrency
Disposability
Dev/Prod Parity
Logs
Admin processes
Cloud Native application patterns
Performance patterns
CQRS pattern
Index table pattern
Throttling pattern
Materialized view pattern
Event sourcing pattern
Resiliency patterns
Bulkhead pattern
Circuit breaker pattern
Retry pattern
Scheduler agent supervisor pattern
Messaging patterns
Publisher subscriber pattern
Piper and filters pattern
Backend to frontend pattern
Security patterns
Valet key pattern
Federated identity pattern
Gate keeper pattern
Encryption/Tokenization
Monitoring patterns
Health end monitoring pattern
Ambassador pattern
Strangler pattern
Anti-corruption layer pattern
Deployment patterns
Service per VM pattern
Single service instance per host pattern
Service instance per container pattern
Multiple service instance per host pattern
Conclusion
Questions
3. Cloud-Native Options Available in Azure
Structure
Objective
Azure platform to develop Cloud Native applications
Cloud Native Applications - Options available in Azure
Azure App Service
Web applications
WebJobs
Mobile Apps
Creating a new mobile app
Logic apps
API apps
Azure functions
Artificial Intelligence Services
Cognitive Services
Conversational Services - BOTs
Custom AI - Machine Learning
Workflow for creating machine learning models:
Development - Using Azure Machine Learning service UI-based, low-code experience
ML.NET
Consume the model
Accord.NET Image Processing and Machine Learning Framework
Scientific computing
Signal and image processing
Support libraries
IOT applications
Devices
Device and Event Processing
Data Visualization
Integration, Messaging, and Events
Conclusion
Questions
4. BOT Framework Basics
Structure
Objective
Introduction to Bots
Relevancy of Bots
Bots to Consumers
Bots to Enterprises
Microsoft Bot Framework
Developing a Simple BOT
Setup and Configuration
Startup.cs
Program.cs
appsettings.json file
EmptyBot.cs
Bot Controller
Extending a Bot with LUIS
Important constructs are as follows:
The process of creating a model
Adding LUIS to our example
Conclusion
References
5. Developing Cloud Native Applications Leveraging Microservices
Structure
Objective
Basics of microservices
Best practices
Developing Microservices in Azure
Azure Service Fabric
Service Fabric Programming Models
Managing and Inspecting Azure Service Fabric clusters
Cloud Services
Azure Kubernetes Service
Azure API Management
The API Management Gateway
Admin portal
The developer portal
Developing a simple application using APIs and microservices
For testing the new APIM API in the Azure portal:
Azure API Management Policies
Configure scope
Managing Security in API Management
Conclusion
Questions
6. Developing Integration Capabilities Using Serverless Architecture
Structure
Objective
Why is integration capability required?
Serverless Computing
Integration options with Azure
Azure Logic Apps
Connectors for Azure Logic Apps:
Azure Service Bus
Azure Functions
Application patterns
Azure IoT Hub
Event Grid
Developing simple workflows and bringing it all together
Example Use Case 1
Example Use Case 2
Conclusion
Questions
7. Developing IoT Application
Introduction to Internet of Things (IoT)
Structure
Objective
Influence of IoT applications on various industries
Stakeholders of IoT
Benefits of IOT
IoT challenges today
Information flow in an IoT scenario
Azure IoT reference architecture
Azure IoT logical reference architecture
Edge (things)
Platform (insights)
Enterprise (actions)
User management
IoT Edge devices
Data transformation
Machine learning system
Security
Logging and monitoring
High availability
Azure IoT Architecture Systems
Devices
Device connectivity
Field gateway (edge devices)
Cloud gateway
Storage
Data flow and stream processing
Monitoring and logging
Logging
Business systems integration
Machine Learning
Developing IoT application in Azure
Devices
Backend services
Communication
Azure IoT services
Platform services
Edge and device software
Setup the IoT Hub
Logging into Azure Portal
Size and Scale
Review + create
Shared access policies
Message Routing for an IoT Hub
Routes
Data source
Create a storage account
Custom endpoints
Find a specific IoT Hub
Connect a device and send and receive messages (Platform)
Create a device
Register a device
Message to device
Metrics
Work with a device twin for device management (Platform)
Device Twin
Visualize IoT data using the Time Series Insight
Consumer groups
Time Series Insights Environment
IoTHub Consumer Group
Time Stamp
Review Time Series Insights Environment
Deployment
Conclusion
Questions
8. Developing a Sample ML Based Application
Structure
Objective
What is Machine Learning?
History of where it all began
Why is Machine Learning important?
Machine Learning Paradigms
Supervised Learning
Unsupervised learning
Reinforcement Learning
Semi-supervised learning
Machine Learning with Microsoft Azure
Azure Machine Learning Studio
Azure Machine Learning Service
Microsoft Machine Learning Server
SQL Server Machine Learning Services
Development platforms and tools
Azure Databricks
Azure Data Science Virtual Machine
ML.NET
Tools
Frameworks
Cognitive services - Pre-trained models
Vision
Speech
Language
Knowledge
Search
Creating a machine learning model using Azure Machine Learning Studio
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Step 8
Step 9
Step 10
Step 11
Step 12
Closing note
Conclusion
9. Enterprise Use Cases for Digital Transformation
Structure
Objective
Goal of enterprise digital transformation
Oil and gas industry transformation
Business scenario
Upstream business processes
Downstream business processes
System architecture
Enterprise information resources
Operational system layer
Azure application framework services
Azure Integration Layer
Business process layer
Data analysis and reporting
Access layer
Channel layer
Client layer
Management and monitoring
Results
License management system
Business scenario
Domain driven design
License management system architecture
Citizenregistration microservice
License Request Microservice
Fee processing microservice
License processing
Inspection
Circuit breaker
Azure Cloud Configuration
Messaging andevents stream
Monitoring
Distributed Tracing
Azure Security
Backing services
Results
Smart Campus
Business scenario
System architecture
User Access Layer
Channel layer
Business logic layer
Big data layer
Cloud Computing Layer
Network Communication Layer
IntelliSense Layer
System operations and common services
Core sharing applications/platforms
Security services
Results
Conclusion
Questions

Systemvoraussetzungen

Als PDF speichern Als Link merken