Co-Founder Taliferro
Introduction
The longer I am away from Azure, the harder it is for me to remember all the components I used to work with. Thus I am creating this post as a cheat sheet. A brain dump that can be useful to future me and any architects who want a quick understandting of some of the Azure products.
Products
Virtual Network - in the Azure environment, a virtual network is established as the platform for data, middleware execution, and resource retrieval. All the resources will communicate outbound to the internet and inbound with Azure components.
Data Private Subnet - A dedicated data subnet within the virtual network. The advantage of breaking the data and app environment up into a subnet is that the subnet allows the division of one network into logical subnetworks, separating data duties from middleware and web resource duties.
App Environment Subnet - This subnet is responsible for all business logic, web service, and resource access.
Azure Active Directory - The Azure Active Directory component serves as an identity management service and can filter access to applications by enforcing security rules. In addition, the AD services allow a project to leverage Single Sign On (SSO).
Azure Network Security Groups (NSG) - An NSG is used to filter network traffic to and from Azure resources in a virtual network.
Azure Data Factory - The Data Factory can issue custom commands to extract data from external sources through the VM. The Azure Data Factory allows a project to create extract-transform-load (ETL) commands that populate a proposed Unified Data Platform (UDP).
Azure Data Lake - The Azure Data Lake works in conjunction with the storage blob for data that is structured, imported through a batch process, and used for streaming analytics and machine learning. The Data Lake is designed to scale across SQL services. If there will be machine learning aspects to a project, the structured data can live here.
Azure Data Store - Used as a repository for data analytics workflow for unstructured, semi-structured, or structured data. It automatically replicates for unexpected hardware failures and has unlimited size and no limits to scale.
Azure Backup Vault - is a cloud-to-cloud implementation where cloud-based VMs and data can be backed up to the Backup Vault without disrupting services.
Virtual Machine (VM) - The virtual machine will act as the gateway for the Azure Data Factory to extract data from external data stores such as ServiceNow, Rally, and so on.
Azure Data Bricks - This can provide the service necessary to harness all the data collected and apply data science to the acquired data.
Azure Storage Blob - If there will be extraction from many systems, the data can grow exponentially, and if the data is possibly unstructured, or the information is in a binary format. A blob is used to store the data and for analysis by other Azure services.
Azure Machine Learning Workspaces - Data scientists use the machine learning workspace to conduct experiments on the data.
Azure SQL database - The SQL database is used to create custom data tables and enforce referential integrity, in addition to running necessary stored procedures to generate the data the UI needs.
Cosmos DB - Cosmos DB can house transformed SQL tables into NoSQL for easy access to middleware. The NoSQL can be a prepackaged summary and lists for easy retrieval, thus decoupling transformation from middleware.
Slots - are used as a way to version deployed code. The active slot will permanently house the most current copy code, while deprecated code is pushed down a slot.
WebApp - The only difference between the web app and API App is that the WebApp does not contain a defined interface. The web app is the nodejs portion of a project.
API App - The API houses the middleware (with interfaces) that contains the business logic that returns the data. Handles all CRUD requests, with the direction to allow the full-range of operations.
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