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Cloud-scale analytics is focused on separating the runtime, automation, and user layers.
Automation to runtime interaction is done using Azure Pipelines and scripted Azure Resource Manager templates.
Important
Cloud-scale analytics uses Azure policies to put boundaries in place and ensure that changes performed by the data landing zone operations teams are compliant.
Cloud-scale analytics uses policies to enforce:
The data landing zone has specific requirements over the standard configuration.
The following diagram shows how automation principles are implemented for a data landing zone.
Cloud-scale analytics consists of:
Each application can evolve independently over time because of different requirements and lifecycles. For example, one of the data landing zones might require RA-GRS storage accounts at some point. It's important to have an infrastructure as code (IaC) representation of each of asset in a repository. This way, changes can be implemented based on requirements in the respective data landing zone and data applications.
The following table summarizes the teams involved in a cloud-scale analytics deployment.
Name | Role | Number of teams |
---|---|---|
Cloud platform team | The Azure cloud platform team in your organization. | One for the whole Azure platform. |
Data platform team | In charge of creating and maintaining Azure Resource Manager template repositories for different levels of cloud-scale analytics. Also maintains the data management landing zone and supports other teams if there are deployment issues or required enhancements. | One for cloud-scale analytics. |
Data landing zone team | In charge of deploying and maintaining a specific data landing zone. Also supports the deployment and enhancement of data applications which produce data products. | One team per data landing zone. |
Data applications team | In charge of data products deployment and updates. | One team per data application. |
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