Azure landing zones for cloud-scale analytics
In response to the need of frictionless governance and platform to actionable insights to the business, cloud-scale analytics represents a strategic design path and targets the technical state for an Azure analytics and AI environment.
The pattern relies upon distribution of the data and its pipelines across domains. This pattern enables ownership of accessibility, usability, and development. Largely based on these patterns, cloud-scale analytics includes the following capabilities:
- Data lineage
- Data classification
- Data ingestion
- Data Quality
- Access Provisioning
Cloud-scale analytics builds on the Start with Cloud Adoption Framework enterprise-scale landing zones and should be considered a supplement to it.
Cloud-scale analytics builds on top of the Microsoft Cloud Adoption Framework whilst applying our Well-Architected framework lens. Microsoft Cloud Adoption Framework provides prescriptive guidance and best practices on cloud operating models, reference architecture, and platform templates. It's based on real-world learnings from some of our most challenging, sophisticated, and complex environments.
Cloud-scale analytics paves the way for customers to build and operationalize landing zones to host and run analytics workloads. You build the landing zones on the foundations of security, governance, and compliance. They're scalable and modular while supporting autonomy and innovation.
Cloud-scale analytics considers five critical design areas that help translate organizational requirements to Azure constructs and capabilities. Lack of attention to these design areas typically creates dissonance and friction between the enterprise-scale definition and Azure adoption. Cloud-scale analytics uses these design areas to help address the mismatch between on-premises and cloud-design infrastructure.
To learn more, see:
Data management landing zone
At the heart of cloud-scale analytics, is its management capability. This capability is enabled through the data management landing zone.
The Data management landing zone is a subscription that governs the platform and supports the following capabilities:
- Data catalog
- Data quality
- Data modeling repository
- Master data management
- API catalog
- Data sharing and contracts
- Data privacy for cloud-scale analytics in Azure
- Provision security for cloud-scale analytics in Azure
For more information, see Overview of the cloud-scale analytics data management landing zone.
Data landing zone
Data landing zones are subscriptions that host multiple analytics and AI solutions relevant to their respective domain or domain(s). These subscriptions within cloud-scale analytics represent primary business groups, integrators, and enablers. These groups own, operate, and often provide innate understanding for the source systems.
A few important points to keep in mind about data landing zones:
- Automated Ingestion capabilities can exist in each data landing zone. These capabilities allow subject matter experts to pull in external data sources into the data landing zone.
- A data landing zone is instantiated based on its core architecture. It includes key capabilities to host an analytics platform.
- A data landing zone can contain multiple data products.
For more information, see Data landing zone.
A data product is anything that drives business value and is pushed to a polyglot store such as the data landing zone data lake.
Data products manage, organize, and make sense of the data within and across domains. A data product is a result of data from one or many transactional system integrations or other data products.
For more information, see cloud-scale analytics data products in Azure.
When ingesting data from operational systems into a read data source. Apart from data quality checks and other applied data, the data should avoid having other data transformations applied to it. This drives reusability of the data product and allow other domains to consume, subject to access, for there use cases as opposed to having multiple extractions from the same operational system.
Cloud-scale analytics is designed with operational excellence at its core through self-service enablement, governance, and streamlined deployments. The working model for data operations enables these core principles by using infrastructure-as-code and deployment templates. It also uses deployment processes that include a forking and branching strategy and a central repository.
For more information, see Organize Operations.
Other design considerations
To get started with the data management and data management landing zones you need to make sure that you have the underpinning architectural components to enable a successful deployment:
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