Update DMA to Cloud-scale analytics (#127)
* Update DMA to Cloud-scale analytics * Update README.md * Update README.md * Update docs/reference/portal.dataProduct.json * Update README.md * Update README.md Co-authored-by: Marvin Buss <34542414+marvinbuss@users.noreply.github.com>
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@ -4,7 +4,7 @@ This project welcomes community contributions in the form of [Issues](#issue-rep
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> If you have a question, have discovered an issue or perhaps you would like to propose an enhancement or idea, please file an issue report **BEFORE** commencing work on it.
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## Contribution scope for Data Management & Analytics Scenario
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## Contribution scope for Cloud-scale Analytics Scenario
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The following is the scope of contributions to this repository:
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@ -90,7 +90,7 @@ After the pull request has been reviewed and approved, the feature branch will b
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## Stewardship of the code repository
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The Data Management & Analytics Scenario code repository are maintained by the code owners. The repository including language, design, policy oversight, and reference implementations. Elevated privileges will be solely based on active contributions in the project repository.
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The Cloud-scale Analytics Scenario code repository are maintained by the code owners. The repository including language, design, policy oversight, and reference implementations. Elevated privileges will be solely based on active contributions in the project repository.
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User may be given elevated privileges once approved by the currents members. All members abide by all organizational polices, including the code of conduct. Elevated privileges can be withdrawn in the case of written notice of resignation or if the member is unreachable or unresponsive for extended time.
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README.md
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README.md
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# Data Management & Analytics Scenario - Data Product Analytics
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# Cloud-scale Analytics Scenario - Data Product Analytics
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## Objective
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The [Data Management & Analytics Scenario](https://aka.ms/adopt/datamanagement) 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.
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The [Cloud-scale Analytics Scenario](https://aka.ms/adopt/cloudscaleanalytics) 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.
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The Data Management & Analytics architecture consists of two core building blocks:
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The Cloud-scale Analyticsarchitecture consists of two core building blocks:
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1. *Data Management Landing Zone* which provides all data management and data governance capabilities for the data platform of an organization.
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1. *Data Landing Zone* which is a logical construct and a unit of scale in the Data Management & Analytics architecture that enables data retention and execution of data workloads for generating insights and value with data.
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1. *Data Landing Zone* which is a logical construct and a unit of scale in the Cloud-scale Analyticsarchitecture that enables data retention and execution of data workloads for generating insights and value with data.
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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. Data Management & Analytics Scenario 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.
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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 Scenario 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.
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---
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_The Data Management & Analytics Scenario represents the strategic design path and target technical state for your Azure data platform._
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*The Cloud-scale Analytics Scenario represents the strategic design path and target technical state for your Azure data platform.*
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---
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@ -23,13 +23,13 @@ This repository describes a Data Product template for Data Analytics and Data Sc
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> **Note:** Before getting started with the deployment, please make sure you are familiar with the [complementary documentation in the Cloud Adoption Framework](https://aka.ms/adopt/datamanagement). Also, before deploying your first Data Product, please make sure that you have deployed a [Data Management Landing Zone](https://github.com/Azure/data-management-zone) and at least one [Data Landing Zone](https://github.com/Azure/data-landing-zone). The minimal recommended setup consists of a single [Data Management Landing Zone](https://github.com/Azure/data-management-zone) and a single [Data Landing Zone](https://github.com/Azure/data-landing-zone).
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## Deploy Data Management & Analytics Scenario
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## Deploy Cloud-scale Analytics Scenario
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The Data Management & 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 Data Management & 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.
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The Cloud-scale Analyticsarchitecture 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 Analyticsarchitecture 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.
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| Reference implementation | Description | Deploy to Azure | Link |
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|:---------------------------|:------------|:----------------|------|
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| Data Management & Analytics Scenario | Deploys a [Data Management Landing Zone](https://github.com/Azure/data-management-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. |[](https://portal.azure.com/#blade/Microsoft_Azure_CreateUIDef/CustomDeploymentBlade/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-management-zone%2Fmain%2Fdocs%2Freference%2FdataManagementAnalytics.json/uiFormDefinitionUri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-management-zone%2Fmain%2Fdocs%2Freference%2Fportal.dataManagementAnalytics.json) | |
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| Cloud-scale Analytics Scenario | Deploys a [Data Management Landing Zone](https://github.com/Azure/data-management-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. |[](https://portal.azure.com/#blade/Microsoft_Azure_CreateUIDef/CustomDeploymentBlade/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-management-zone%2Fmain%2Fdocs%2Freference%2FdataManagementAnalytics.json/uiFormDefinitionUri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-management-zone%2Fmain%2Fdocs%2Freference%2Fportal.dataManagementAnalytics.json) | |
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| Data Management Landing Zone | Deploys a single Data Management Landing Zone to a subscription. |[](https://portal.azure.com/#blade/Microsoft_Azure_CreateUIDef/CustomDeploymentBlade/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-management-zone%2Fmain%2Finfra%2Fmain.json/uiFormDefinitionUri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-management-zone%2Fmain%2Fdocs%2Freference%2Fportal.dataManagementZone.json) | [Repository](https://github.com/Azure/data-management-zone) |
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| Data Landing Zone | Deploys a single Data Landing Zone to a subscription. Please deploy a [Data Management Landing Zone](https://github.com/Azure/data-management-zone) first. |[](https://portal.azure.com/#blade/Microsoft_Azure_CreateUIDef/CustomDeploymentBlade/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-landing-zone%2Fmain%2Finfra%2Fmain.json/uiFormDefinitionUri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-landing-zone%2Fmain%2Fdocs%2Freference%2Fportal.dataLandingZone.json) | [Repository](https://github.com/Azure/data-landing-zone) |
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| 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](https://github.com/Azure/data-management-zone) and [Data Landing Zone](https://github.com/Azure/data-landing-zone) first. |[](https://portal.azure.com/#blade/Microsoft_Azure_CreateUIDef/CustomDeploymentBlade/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-product-batch%2Fmain%2Finfra%2Fmain.json/uiFormDefinitionUri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fdata-product-batch%2Fmain%2Fdocs%2Freference%2Fportal.dataProduct.json) | [Repository](https://github.com/Azure/data-product-batch) |
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# Data Product Analytics - Prerequisites
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This template repository contains all templates to deploy a Data Product for Analytics and Data Science inside a Data Landing Zone of the Data Management & Analytics Scenario. Data Products are another unit of scale inside a Data Landing Zone and provide environments to cross-functional teams to work on individual data use-cases. Hence, this template qualifies for the following usage:
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This template repository contains all templates to deploy a Data Product for Analytics and Data Science inside a Data Landing Zone of the Cloud-scale Analytics Scenario. Data Products are another unit of scale inside a Data Landing Zone and provide environments to cross-functional teams to work on individual data use-cases. Hence, this template qualifies for the following usage:
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| Scenario | Applicability |
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|:-----------------|:-------------------|
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"view": {
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"kind": "Form",
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"properties": {
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"title": "Data Management & Analytics Scenario - Data Product Analytics",
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"title": "Cloud-scale Analytics Scenario - Data Product Analytics",
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"steps": [
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{
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"name": "basics",
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"type": "Microsoft.Common.InfoBox",
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"visible": true,
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"options": {
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"text": "Data Management & Analytics Scenario is a prescriptive reference architecture for data with reference implementation provided by Microsoft. Visit 'aka.ms/adopt/datamanagement' for more details about the solution pattern.",
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"text": "Cloud-scale Analytics Scenario is a prescriptive reference architecture for data with reference implementation provided by Microsoft. Visit 'aka.ms/adopt/datamanagement' for more details about the solution pattern.",
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"style": "Info",
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"uri": "https://aka.ms/adopt/datamanagement"
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"uri": "https://aka.ms/adopt/cloudscaleanalytics"
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}
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},
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{
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"type": "Microsoft.Common.InfoBox",
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"visible": "[equals(steps('generalSettings').machineLearningDeploymentSettings.enableDatabricks, 'yes')]",
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"options": {
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"text": "As per Data Management & Analytics Scenario recommendation, we are advising to use shared Databricks workspaces. Please select the shared product Databricks workspace in your Data Landing Zone and provide an access token to connect the workspace with Machine Learning. Leave blank, if you don't want to connect Databricks and Machine Learning.",
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"text": "As per Cloud-scale Analytics Scenario recommendation, we are advising to use shared Databricks workspaces. Please select the shared product Databricks workspace in your Data Landing Zone and provide an access token to connect the workspace with Machine Learning. Leave blank, if you don't want to connect Databricks and Machine Learning.",
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"style": "Info",
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"uri": "https://docs.microsoft.com/azure/machine-learning/how-to-attach-compute-targets#databricks"
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}
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