This example scenario demonstrates how to use Azure Synapse Analytics with the extensive family of Azure Data Services to build a modern data platform that's capable of handling the most common data challenges in an organization. Show
The solution described in this article combines a range of Azure services that will ingest, store, process, enrich, and serve data and insights from different sources (structured, semi-structured, unstructured, and streaming). Potential use casesThis approach can also be used to:
ArchitectureDownload a Visio file of this architecture.
Note
Deploy the architectureThis deployment accelerator gives you the option to implement the entire reference architecture or choose what workloads you need for your analytics use case. You also have the option to select whether services are accessible via public endpoints or if they are to be accessed only via private endpoints.
Use the following button to deploy the reference using the Azure portal. Run the following command to deploy the entire reference architecture using public endpoints. Click the Try it button to use an embedded shell. az deployment group create --resource-group azsynapse-e2e \ --template-uri https://raw.githubusercontent.com/Azure/azure-synapse-analytics-end2end/main/Deploy/AzureAnalyticsE2E.json \ --parameters networkIsolationMode=default synapseSqlAdminPassword=use-complex-password-hereRun the following command to deploy the entire reference architecture using private endpoints. Click the Try it button to use an embedded shell. az deployment group create --resource-group azsynapse-e2e \ --template-uri https://raw.githubusercontent.com/Azure/azure-synapse-analytics-end2end/main/Deploy/AzureAnalyticsE2E.json \ --parameters networkIsolationMode=vNet synapseSqlAdminPassword=use-complex-password-hereFor detailed information and additional deployment options, see the deployment accelerator GitHub repo with documentation and code used to define this solution. Analytics use casesThe analytics use cases covered by the architecture are illustrated by the different data sources on the left-hand side of the diagram. Data flows through the solution from the bottom up as follows: Azure data services, cloud native HTAP with Cosmos DB and DataverseProcessStore
Serve
Relational databasesIngest
Store
Process and enrichServe
Semi-structured data sourcesIngest
Store
Process and enrich
Serve
Non-structured data sourcesIngest
Store
Process and enrich
Serve
StreamingIngest
StoreProcess and enrichServe
Discover and governData governance is a common challenge in large enterprise environments. On one hand, business analysts need to be able to discover and understand data assets that can help them solve business problems. On the other hand, Chief Data Officers want insights on privacy and security of business data. Microsoft Purview
Platform servicesIn order to improve the quality of your Azure solutions, follow the recommendations and guidelines defined in the Azure Well-Architected Framework five pillars of architecture excellence: Cost Optimization, Operational Excellence, Performance Efficiency, Reliability, and Security. Following these recommendations, the services below should be considered as part of the design:
ComponentsThe following Azure services have been used in the architecture: AlternativesThe technologies in this architecture were chosen because each of them provides the necessary functionality to handle the most common data challenges in an organization. These services meet the requirements for scalability and availability, while helping them control costs. The services covered by this architecture are only a subset of a much larger family of Azure services. Similar outcomes can be achieved by using other services or features not covered by this design. Specific business requirements for your analytics use cases may also ask for the use of different services or features not considered in this design. Similar architecture can also be implemented for pre-production environments where you can develop and test your workloads. Consider the specific requirements for your workloads and the capabilities of each service for a cost-effective pre-production environment. Cost optimizationIn general, use the Azure pricing calculator to estimate costs. The ideal individual pricing tier and the total overall cost of each service included in the architecture is dependent on the amount of data to be processed and stored and the acceptable performance level expected. Use the guide below to learn more about how each service is priced:
ContributorsThis article is being updated and maintained by Microsoft. It was originally written by the following contributors. Principal authors:
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