Enterprise-Scale Analytics and AI - Data Landing Zone
Objective
The Enterprise-Scale Analytics architecture provides a prescriptive data platform design coupled with Azure best practices and design principles. These principles serve as a compass for subsequent design decisions across critical technical domains. The architecture will continue to evolve alongside the Azure platform and is ultimately driven by the various design decisions that organizations must make to define their Azure data journey.
The Enterprise-Scale Analytics architecture consists of two core building blocks:
- Data Management Zone which provides all data management and data governance capabilities for the data platform of an organization.
- Data Landing Zone which is a logical construct and a unit of scale in the Enterprise-Scale Analytics architecture that enables data retention and execution of data workloads for generating insights and value with data.
The architecture is modular by design and allows organizations to start small with a single Data Management Zone and Data Landing Zone, but also allows to scale to a multi-subscription data platform environment by adding more Data Landing Zones to the architecture. Thereby, the reference design allows to implement different modern data platform patterns like data-mesh, data-fabric as well as traditional datalake architectures. Enterprise-Scale Analytics has been very well aligned with the data-mesh approach, and is ideally suited to help organizations build data products and share these across business units of an organization. If core recommendations are followed, the resulting target architecture will put the customer on a path to sustainable scale.
The Enterprise-Scale Analytics architecture represents the strategic design path and target technical state for your Azure data platform.
This respository describes the Data Landing Zone, which is where data is persisted and data workloads are executed. A Data Landing Zone is a unit of scale of the Enterprise-Scale Analytics architecture pattern and it enables regional deployments, clear seperation of ownership, chargeback of cost, in-place data sharing within and across Data Landing Zones and many other much asked benefits. In addition, it is possible to scale within Data Landing Zones with cross-functional Data Integration and Data Product teams. The reference design targets a self-service approach for these teams to overcome bottlenecks and the need for a central team for cloud service deployments. The Data Landing Zone reference implementation will create a consistent setup inside a subscription and will deploy storage accounts as well as data processing services like Azure Synapse, Azure Data Factory as well as Azure Databricks.
Note: Before getting started with the deployment, please make sure you are familiar with the complementary documentation in the Cloud Adoption Framework. Also, before deploying your first Data Landing Zone, please make sure that you have deployed a Data Management Zone. The minimal recommended setup consists of a single Data Management Zone and a single Data Landing Zone.
Deploy Enterprise-Scale Analytics
The Enterprise-Scale Analytics architecture is modular by design and allows customers to start with a small footprint and grow over time. In order to not end up in a migration project, customers should decide upfront how they want to organize data domains across Data Landing Zones. All Enterprise-Scale Analytics architecture building blocks can be deployed through the Azure Portal as well as through GitHub Actions workflows and Azure DevOps Pipelines. The template repositories contain sample YAML pipelines to more quickly get started with the setup of the environments.
| Reference implementation | Description | Deploy to Azure | Link |
|---|---|---|---|
| Enterprise-Scale Analytics | Deploys a Data Management Zone and one or multiple Data Landing Zones all at once. Provides less options than the the individual Data Management Zone and Data Landing Zone deployment options. Helps you to quickly get started and make yourself familiar with the reference design. For more advanced scenarios, please deploy the artifacts individually. | ||
| Data Management Zone | Deploys a single Data Management Zone to a subscription. | Repository | |
| Data Landing Zone | Deploys a single Data Landing Zone to a subscription. Please deploy a Data Management Zone first. | Repository | |
| Data Product Batch | Deploys a Data Workload template for Data Batch Analysis to a resource group inside a Data Landing Zone. Please deploy a Data Management Zone and Data Landing Zone first. | Repository | |
| Data Product Streaming | Deploys a Data Workload template for Data Streaming Analysis to a resource group inside a Data Landing Zone. Please deploy a Data Management Zone and Data Landing Zone first. | Repository | |
| Data Product Analytics | Deploys a Data Workload template for Data Analytics and Data Science to a resource group inside a Data Landing Zone. Please deploy a Data Management Zone and Data Landing Zone first. | Repository |
Deploy Data Landing Zone
To deploy the Data Landing Zone into your Azure Subscription, please follow the step-by-step instructions:
- Prerequisites
- Create repository
- Setting up Service Principal
- Template Deployment
- Known Issues
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.