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Template to deploy a single Data Landing Zone of the Data Management & Analytics Scenario (former Enterprise-Scale Analytics). The Data Landing Zone is a logical construct and a unit of scale in the architecture that enables data retention and execution of data workloads for generating insights and value with data.

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Cloud-scale Analytics Scenario - Data Landing Zone

Objective

The Cloud-scale Analytics Scenario 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 Cloud-scale Analytics architecture consists of two core building blocks:

  1. Data Management Landing Zone which provides all data management and data governance capabilities for the data platform of an organization.
  2. Data Landing Zone which is a logical construct and a unit of scale in the Cloud-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 Landing 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. Cloud-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.

Cloud-scale Analytics


The Cloud-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 Cloud-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 Landing Zone. The minimal recommended setup consists of a single Data Management Landing Zone and a single Data Landing Zone.

Deploy Cloud-scale Analytics

The Cloud-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 Cloud-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
Cloud-scale Analytics Scenario Deploys a Data Management Landing Zone and one or multiple Data Landing Zones all at once. Provides less options than the the individual Data Management Landing 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. Deploy To Azure
Data Management Landing Zone Deploys a single Data Management Landing Zone to a subscription. Deploy To Azure Repository
Data Landing Zone Deploys a single Data Landing Zone to a subscription. Please deploy a Data Management Landing Zone first. Deploy To Azure 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 Landing Zone and Data Landing Zone first. Deploy To Azure 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 Landing Zone and Data Landing Zone first. Deploy To Azure 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 Landing Zone and Data Landing Zone first. Deploy To Azure Repository

Deploy Data Landing Zone

To deploy the Data Landing Zone into your Azure Subscription, please follow the step-by-step instructions:

  1. Prerequisites
  2. Create repository
  3. Setting up Service Principal
  4. Template Deployment
    1. GitHub Action Deployment
    2. Azure DevOps Deployment
  5. Known Issues

Contributing

Please review the Contributor's Guide for more information on how to contribute to this project via Issue Reports and Pull Requests.

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Template to deploy a single Data Landing Zone of the Data Management & Analytics Scenario (former Enterprise-Scale Analytics). The Data Landing Zone is a logical construct and a unit of scale in the architecture that enables data retention and execution of data workloads for generating insights and value with data.

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