Developing Cloud Native Applications in Azure using .NET Core: A Practitioner’s Guide to Design, Develop and Deploy Apps

by Dr Gopala Krishna Behara, Rekha Kodali, Sankara Narayanan Govindarajulu

Length: 322 pages
Edition: 1
Language: English
Publisher: BPB Publications
Publication Date: 2020-01-31
ISBN-10: 9389328748
ISBN-13: 9789389328745
Sales Rank: #3395513 (See Top 100 Books)

Guide to designing and developing cloud native applications in Azure

Key Features

Basics of Cloud Native Applications
Designing Microservices
Different cloud native options for developing Cloud Native Applications in Azure
BOTs, Web Apps, Mobile Apps, Logic Apps, Service Bus, Azure Functions
Azure IOT Applications
Azure Machine Learning Basics
Enterprise Digital Journeys

Description
The mainstreaming of the cloud-native architecture as an enterprise discipline is well underway. According to the Forbes report, in January 2018, 83% of enterprise workloads will be in the cloud by 2020, 41% of enterprise workloads will run on public cloud platforms while another 22% will be running on hybrid cloud platforms.
Customers are embarking on enterprise digital transformation journeys. Adopting cloud, cloud-native architectures, and microservices is an important aspect of the journey.
This book starts with a brief introduction to the basics of cloud-native applications and cloud-native application patterns. It covers cloud-native options available in Azure.
The objective of the book is to provide practical guidelines to an architect/designer/consultant/developer who is part of the Cloud application definition team. The book articulates a methodology that the implementation team needs to follow in a systematic manner and adapt them to fulfill the requirements for enabling the cloud-native application. It emphasizes on the interpersonal skills and techniques for organizing and directing the cloud-native definition, leadership buy-in, and leading the transition from planning to implementation. It also highlights steps to be followed and the patterns for developing cloud-native applications, cloud-native options available in Azure, developing BOT, and microservices based on Azure. It also covers how to develop simple IoT applications, Machine learning-based applications, and the serverless architecture using Azure with a practical and pragmatic approach.
This book embraces a structured approach around the following key themes that represent the typical phases an enterprise traverses during its cloud-native application journey.

What will you learn
This book aims to:

Demonstrate the importance of cloud-native applications in elevating the effectiveness of organizational transformation programs and digital enterprise journeys using MS Azure.
Disseminate current advancements and thought leadership in the area of cloud-native architecture in the context of digital enterprises.
Provide initiatives with evidence-based, credible, field-tested and practical guidance in designing their respective architectures.

Who this book is for
The book is intended for anyone looking for a career in Cloud technology, especially all aspiring Cloud Architects who want to learn cloud-native architectures, Microservices, IoT, BOT and Microsoft Azure platform.

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

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