When we talk about Azure architectures for data warehousing or analytics, we usually show a diagram that looks like the below.
This diagram is a great start to explain what services will be used in Azure to build out a solution or platform. But many times, we add the specific resource names and stop there. If you have built several projects in Azure, you will know there some other things for which you will need to plan. So what’s missing?
Azure Active Directory
Let’s start with Azure Active Directory (AAD). In order to provision the resources in the diagram, your Azure subscription must already be associated with an Active Directory. AAD is Microsoft’s cloud-based identity and access management service. Members of an organization have a user account that can sign in to various services. AAD is used to access Office 365, Power BI, and Dynamics 365, as well as the Azure portal. It can also be used to grant access and permissions to specific Azure resources.
For instance, users who will participate in Data Factory pipeline development must be assigned to the Data Factory Contributor role in Azure. Users can authenticate via AAD to log in to Azure SQL DW. You can use AD Groups to grant permissions to users interacting with various Azure resources such as Azure SQL DW or SQL DB as well as Azure Analysis Services and Power BI. It’s more efficient to manage permissions for groups than to do it for each user. You’ll need a plan to manage group membership.
You may also need to register applications in Azure Active Directory to allow Azure services to authenticate and interact with each other. While the guidance for many services is now to use managed identities, this may not be available for every situation just yet.
If your organization has some infrastructure on premises, it is likely that they have Active Directory on premises as well. So you will want to make sure you have a solution in place to sync your on-premises and Azure Active Directory.
Virtual Networks (or VNets) allow many types of Azure resources to securely communicate with each other, the internet, and on-premises networks. You can have multiple virtual networks in an Azure subscription. Each virtual network is isolated from other VNets, unless you set up VNet peering.
Some PaaS services such as Azure Storage Accounts, Azure SQL DW, and Azure Analysis Services support Virtual Network Service Endpoints. A common usage scenario is to set up VNets and VNet Service Endpoints to connect resources to on-premises networks. Some organizations prefer to use VNet Service Endpoints instead of public service endpoints, making it so that traffic can only access the resource from within the organization’s local network.
In order to connect a VNet to an on-premises network or another VNet (outside of peering), you’ll need a VPN Gateway. You’ll need to identify the most appropriate type of VPN Gateway: Point-to-Site, Site-to-Site, or Express Route. These offerings differ based on bandwidth, protocols supported, routing, connection resiliency, and SLAs. Pricing can vary greatly based upon your gateway type.
While VNets and VPN Gateways are probably the most common networking resources in Azure, there are many other networking services and related design decisions to consider as you plan an Azure deployment.
Your BI solution may be entirely in Azure, but if you need to retrieve data from data sources in a private network (on premises or on a VNet), you’ll need a gateway. If you are using Azure Data Factory, you’ll need a Self-hosted Integration Runtime (IR). If the source for your Power BI or Azure Analysis Services model is on a private network, you’ll need an On-Premises Data Gateway. You can use the same gateway to connect to Analysis Services, Power BI, Logic Apps, Power Apps, and Flow. If you will have a mix of Analysis Services and Power BI models sharing the same gateway, you’ll need to make sure that your Power BI region and your AAS region match.
These gateways require that you have a machine inside the private network on which to install them. And if you want to scale out or have failover capabilities, you may need multiple gateways and multiple VMs. So while you may be building a solution in Azure, you might end up with a few on-premises VMs to be able to securely move source data.
Dev and Test Environments
Our nice and tidy diagram above is only showing production. We also need at least a development environment and maybe one or more test environments. You’ll need to decide how to design your dev/test environments. You may want duplicate resources in a separate resource group; e.g. a dev resource group that contains a dev Data Factory, a dev Azure SQL DW, a dev Azure Analysis Services, etc. While separating environments by resource group is common, it’s not your only option. You will need to decide if you prefer to separate environments by resource group, subscription, directory, or some combination of the three.
ARM templates and PowerShell are very useful for deploying updates and creating new environments. Also, take a look at Azure Blueprints.
You’ll also want to investigate ways to keep the costs of non-prod environments down via smaller-sized resources or pausing or deleting resources where applicable.
Plan For, Don’t Worry About, the Extras
There are several other ancillary Azure services that could/should be part of your solution.
- For source control, GitHub and Azure Repos have the easiest integration, especially with Azure Data Factory. You’ll not only want source control for things like database projects and Data Factory pipelines, but also possibly for ARM templates and PowerShell scripts used to deploy resources (think: infrastructure as code).
- You’ll want to set up Azure Monitoring, including alerts to let you know when processes and services are not running as intended.
- If you want more cost management support than just setting a spending limit on a subscription (if it is available for your subscription type), it may be helpful to set budgets in Azure so you can be notified when you hit certain percentages or amounts.
Be sure to think through the entire data/solution management lifecycle. You may want to include extra services for cataloging, governing, and archiving your data.
This may sound like a complex list, but these resources and services are fairly easy to work with. Azure Active Directory has a user-friendly GUI in the portal, and commands can be automated with PowerShell, requiring relatively little effort to manage users and groups to support your BI environment. VNets and VPN Gateways are a little more complex, but there are step-by-step instructions available for many tasks in the Microsoft Docs. The Power BI Gateway and ADF IR have quick and easy GUI installers that just take a few minutes. You can automate Azure deployments with Azure Pipelines or PowerShell scripts.
None of these things are really that awful to implement or manage in Azure, unless you weren’t aware of them and your project is paused until you can get them set up.
Is there anything else you find is commonly left out when planning for BI solutions in Azure? Leave me a comment if you would like to add to the list.
Update (1/26/19): Helpful readers have commented on other aspects of your Azure BI architecture they felt were often overlooked:
- Make sure you have a plan for how to process your Azure Analysis Services model. Will you use Azure Automation? Call an Azure Function from Data Factory?
- Be sure to organize and tag your resources appropriately to help you understand and control costs.
- Don’t forget Azure Key Vault. This will help you keep keys and passwords secure.
(Thanks to Chad Toney, Shannon Holck, and Santiago Cepas for these suggestions.)
6 thoughts on “The Necessary Extras That Aren’t Shown in Your Azure BI Architecture Diagram”
Great post Meagan. You are missing Key Vault! No meaningful Azure deployment lacks that.
Ah, yes. Good addition. Thanks!
Great post! We’ve also discovered that tagging your Azure resources is critical to tracking which POCs and projects are costing you the most and find ways to save $$. Resource groups don’t always work well, especially when you add Databricks since the clusters seem to generate on their own (maybe this is improving). I also might mention that the diagram is great if you have BIG data, but if your projects are smaller or POCs you might be able to use ML Studio instead of Databricks and a plain old Sql db instead of DW.
Hi, Shannon. Great points. To be clear, I’m not suggesting that everyone use Databricks and Azure SQL DW for every scenario. I just used the diagram as an example of what typically gets included in an Azure BI architecture diagram. In that particular architecture, Databricks is being used both for data prep before loading to the DW as well as data science. I don’t think ML Studio would be a good fit for both of those use cases. But your general point is well taken.
Thanks for such a valuable informative post on Azure. By going through it most of my ongoing doubts regarding Azure have been cleared. Looking forward for more of such informative posts.
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