Azure Automation, Azure DevOps Services, and/or Terraform (Optional).

1. • Azure offers built-in automation tools (Azure Automation & Azure DevOps Services) for managing Azure resources.

   • Terraform (Optional and works with Azure) is a separate, open-source IaC (Infrastructure as Code -- Uses configured code (HCL & API) rather than doing it manually). Integrates well with Azure for infrastructure provisioning (provide) and management. 

     1. Terraform code: Terraform uses the "HCL code" to define your infrastructure. It specifies the resources you need and their configurations.

     2. APIs: Terraform uses APIs to interact with cloud platforms and services to create, manage, and destroy resources. Terraform code (aka HCL code) interacts with these APIs behind the scenes.

Azure offers 2 main options, while Terraform itself sits alongside them as a complementary tool: (4+1)

1. Azure Automation (Only Azure): A built-in service for automating tasks and workflows within Azure. It uses runbooks, which are scripts containing PowerShell, Python, or graphical commands, to manage your Azure resources.

   1. • Function: Automation platform.

      • Focus: Executes scripts and workflows to automate various tasks beyond just infrastructure management.

      • Method: Uses PowerShell, Python, or custom scripts to define workflows that can interact with Azure resources, run logic checks, and perform other actions.

      • Best for: Automating repetitive tasks across your Azure environment, including post-deployment configuration, software installation, security checks, and more.

2. Azure Resource Manager (ARM Templates): This is the native IaC solution provided by Azure. ARM templates use JSON or Bicep (a more human-readable syntax) to define the infrastructure resources you want to deploy, such as virtual machines, storage accounts, and databases. 

   1. • Function: Provisioning (supply) and management tool. 

      • Use ARM / Focus: Defines & deploys the infrastructure components of your Azure solution (VMs, dbases, etc.).

      • Method: Uses templates (ARM templates or Bicep) written in JSON or a more human-readable language to describe the desired infrastructure state.

      • Best for: Automating the creation, deletion, and modification of your Azure resources in a consistent and repeatable way.

3. Azure CLI (Azure Command-Line Interface): A toolset that allows you to interact with Azure cloud platform through command-line prompts. It provides a way to manage and automate various Azure resources and services without using the Azure portal interface. Reference: https://learn.microsoft.com/en-us/cli/azure/

   1. • Value: (4)

1. Cross-platform: It works on Windows, macOS, and Linux environments.

2. Automates tasks: To automate deployments and configurations, making it a favorite among developers and DevOps professionals.

3. Manages resources: You can use it to create, manage, and delete Azure resources like VMs, storage accounts, databases, and more.

4. Command-line interface: It requires users to interact with Azure through commands rather than a graphical user interface (GUI).

4. Azure DevOps Services (Pipelines, formerly Azure DevOps): A suite of tools that integrates with various development and deployment lifecycles. You can create pipelines that automate tasks like building, testing, and deploying applications, including infrastructure provisioning.

5. Terraform (Not Azure): Terraform is a popular open-source Infrastructure as Code (IaC) tool that integrates well with Azure. Using auto-configurations via Manually. It allows you to define your infrastructure in configuration files (Code) and then provision and manage those resources across different cloud providers.

1. • Value:

     2. 1. Cloud-agnostic: You can use the same Terraform scripts for Azure and other cloud platforms.

        2. Declarative: You define the desired state of your infrastructure, and Terraform figures out the changes needed.

        3. Version control: Terraform configs can be version controlled like any code, allowing for tracking changes and rollbacks.

Microsoft Fabric: Data Analytics, Data Integration, and Transformation.

• BLUF: 

  1. An integrated data analytics platform offered by Microsoft

  2. Combines the below Azure data services into a single, unified entity, using: (3+1)

     1. Azure Data Factory (aka Data Workflows-Pipelines),  

        • BLUF: For data integration and transformation. It allows you to create and manage data-driven pipelines that automate the movement and processing of data at scale. 

        • Functions (4): (1) Data Integration: ADF can connect to various data sources, both on-premises and in the cloud. This includes relational databases, data warehouses, cloud storage platforms, SaaS applications, and more. You can use ADF to copy data between these different sources easily. (2) Data Transformation: To transform data before loading it into its destination. These transformations can involve cleansing, filtering, sorting, aggregating, or joining data sets. ADF offers a visual data flow interface for building these transformations, along with support for coding using languages like Scala and Python for complex scenarios. (3) Orchestration and Scheduling: Orchestrate the execution of your data pipelines, meaning, you can define dependencies between pipelines and schedule them to run at specific times or trigger them based on events (like new data arriving in a source).(4) Monitoring and Management: Monitor the execution of your pipelines and data flows. You can track the progress of data movement, identify errors, and troubleshoot any issues.

        • Use Cases: (5)

          1. Loading data into data warehouses and data lakes for analytics

          2. Moving data between cloud and on-premises environments

          3. Data cleansing and transformation for improving data quality

          4. Automating data pipelines for ETL (Extract, Transform, Load) processes

          5. Building real-time data integration solutions

     2. Azure Synapse Analytics. 

        • BLUF: To unify data warehousing and big data analytics within a single platform. It allows you to work with both large, structured datasets (data warehouses) and unstructured or complex data (big data) in one place.

        • Features: (4)

          1. Unified Environment: Azure Synapse Analytics combines the strengths of various data technologies:

             • SQL technologies for data warehousing and structured data analysis using familiar T-SQL queries.

             • Apache Spark for big data analytics and processing large, complex datasets.

             • Azure Data Explorer (preview) for analyzing log and time-series data efficiently.

          2. Data Integration: It integrates seamlessly with other Azure services like Azure Data Factory (ADF) for data movement and orchestration. This allows you to easily ingest data from various sources into Synapse Analytics for analysis.

          3. Scalability: Offers elastic scaling, meaning you can scale compute resources up or down based on your workload needs. This helps optimize costs and ensures smooth performance for both small and large-scale analytics tasks.

          4. Security: Offers robust security features of Microsoft Azure, including encryption, access controls, and data governance functionalities.

        • Use Cases: (4)

          1. Enterprise Data Warehousing: Consolidate and analyze large volumes of structured data for business intelligence reporting.

          2. Big Data Analytics: Gain insights from unstructured or complex data sources like social media data, sensor data, or log files.

          3. Machine Learning Workflows: Prepare and manage data for machine learning projects using Azure Synapse Analytics alongside Azure Machine Learning.

          4. Business Analytics and Reporting: Create interactive dashboards and reports to visualize and analyze data for informed decision-making.

     3. Power BI. 

        • BLUF: Transforms your data into visually immersive and interactive insights (Pies & Graphs). Whether your data resides in Excel, a cloud-based platform, or an on-premises data warehouse,. 

     4. Also, see Azure Service Fabric (that can do Microservices, Analytics, & Containers). 

Containers (K8s) & Microservices.

• BLUF: Both are valuable tools for building modern software. Understanding their strengths and how they can work together can help you design and deploy resilient, scalable, and agile applications.

• Containers (K8s, like open-source "Docker"): -- Like a Shipping container.

  1. Function: A container is a standardized unit of software that packages code and all its dependencies (libraries, configuration files) together. This allows the code to run consistently on any system that supports the container format.

  2. Analogy: Think of a container like a shipping container (self-contained, portable, can be deployed on various systems, standardized, and isolated). It holds all the cargo (your code and its dependencies) needed for an application to run, regardless of the ship (the underlying computer system) it's transported on.

  3. Packaging technology: They package software with its dependencies and configuration into a standardized unit, allowing it to run consistently across different environments. Think of it as a portable box for your application.

  4. Focus: They improve portability, isolation, and resource management. You can easily deploy and scale containers on various platforms without worrying about underlying environmental differences.

• Microservices: -- Small-Coded Service

  1. Architectural principle: They are a way of designing software by breaking it down into small, independent services. Each service encapsulates a specific functionality and communicates with other services through well-defined APIs.

  2. Focus: They prioritize agility, scalability, and resilience. Developers can independently design, build, and deploy each service, leading to faster development cycles and easier maintenance.

• Relationship between Containers (K8s) & Microservices:

  1. One container can hold multiple microservices: This is especially common when the microservices share resources or are tightly coupled in functionality.

  2. One microservice can span multiple containers: This is particularly useful for complex services with diverse dependencies or high resource requirements.

  3. A microservice doesn't always need a container: It can be deployed in other ways, like serverless functions or virtual machines, depending on the specific needs.

• Factors to consider:

  1. Complexity and interdependence of microservices: "Tightly coupled" services might benefit from being together in a container, while independent ones might do better separately.

  2. Resource requirements: If microservices have different resource needs, separating them could be more efficient.

  3. Deployment and scaling considerations: If you need to frequently deploy or scale individual services, having them in separate containers offers more flexibility.

Azure API (Application Programming Interface).

• BLUF:

  1. Function: An API acts as a messenger (like a waiter) between different software applications. It provides a set of rules and specifications that define how applications can request and receive data from each other.

  2. Analogy: Imagine a waiter in a restaurant. The waiter takes your order (your request) and delivers it to the kitchen (the other application). The kitchen prepares the food (processes the request) and sends it back to the waiter (sends the data back through the API). You (the user) never interact directly with the kitchen, but you receive what you request through the waiter (the API).

• Tools: (3)

  1. Azure REST API: This is the underlying API used by most Azure services. You can use it directly with programming languages or through SDKs (Software Development Kits) provided by Azure.

  2. Azure API Management: This service acts as a gateway for your APIs, providing features like security, scalability, analytics, and a developer portal. It can be used to manage APIs hosted on Azure, on-premises, or in other clouds.

  3. Azure App Service: This service allows you to host web apps, mobile backends, and APIs. It includes built-in features for authentication, authorization, and API management.

• Resources:

  1. Azure API Apps: https://azure.microsoft.com/en-us/products/app-service/api

  2. Azure API Management: https://azure.microsoft.com/en-us/pricing/details/api-management/

  3. Azure REST API reference documentation: https://learn.microsoft.com/en-us/rest/api/

Azure doesn't have its own "Docker." Instead, Azure fully embraces the open-source "Docker" standard and offers several Azure resources services and features to build around it, providing a complete cloud-based development and deployment platform:

• Azure Container Registry: This is a managed registry for storing and managing your Docker images (and other OCI-compliant images) within Azure. It allows private and secure storage, fast retrieval, and integration with other Azure services for deployment and scaling.

• Docker using Azure Tools: This refers to the entire ecosystem of Azure offerings optimized for running Docker containers. This includes services like:

  1. Azure App Service: To deploy containerized applications (built with Docker) on App Service for hosting and scaling.

  2. Azure Kubernetes Service (AKS): This fully managed Kubernetes cluster service makes deploying and managing containerized applications at scale in a production environment very efficient.

  3. Azure Container Instances (ACI): Allows you to run Docker containers on-demand without managing the underlying infrastructure, making it ideal for short-lived tasks or bursting workloads.

• Leverage other Azure services: Docker containerized applications deployed on Azure can directly integrate with other services like (1) Azure Storage, (2) Azure Functions, (3) Azure Monitor, etc., providing a complete cloud-based development and deployment platform.

Azure Service Fabric: Microservices, Analytics, & Containerization.

• BLUF: (2)

  1. This is a distributed systems platform on Microsoft Azure. It helps developers design, deploy, and manage scalable microservices and containerized applications. 

  2. It provides a framework that takes care of the complexities of distributed systems like scalability, reliability, and service lifecycle management, allowing developers to focus on building the core functionalities of their applications. 

• Examples of how Azure Service Fabric (Microservices & Analytics) can be used: (3)

  1. Building a Microservices-based E-commerce Application: An e-commerce company can leverage Azure Service Fabric to develop its application as a collection of independent microservices. These microservices could handle tasks like product catalog management, shopping cart functionality, and order processing. Each microservice can be scaled independently based on demand, improving the overall responsiveness and scalability of the application.

  2. Developing a Real-time Analytics Platform: A company can use Azure Service Fabric to build a real-time analytics platform that ingests data streams from various sources, analyzes the data, and generates insights in real-time. Azure Service Fabric ensures the platform can handle high volumes of data and reliably deliver real-time insights to users.

  3. Modernizing Legacy Applications: An organization can use Azure Service Fabric to modernize monolithic legacy applications by decomposing them into microservices. This modular approach allows for easier maintenance, scalability, and integration with modern cloud-based technologies.