Delivering Information with Azure Synapse and Data Vault 2.0
Data Vault has been designed to integrate data from multiple data sources, creatively destruct the data into its fundamental components, and store and organize it so that any target structure can be derived quickly. This article focused on generating information models, often dimensional models, using virtual entities. They are used in the data architecture to deliver information. After all, dimensional models are easier to consume by dashboarding solutions, and business users know how to use dimensions and facts to aggregate their measures. However, PIT and bridge tables are usually needed to maintain the desired performance level. They also simplify the implementation of dimension and fact entities and, for those reasons, are frequently found in Data Vault-based data platforms. This article completes the information delivery. The following articles will focus on the automation aspects of Data Vault modeling and implementation.
Unable to process AAS model connecting to Azure SQL with Service Account
Hello I have built a demo SSAS model that I am hosting on an Azure Analysis Services Server. The model connects to an Azure SQL database in my tenant (the Database is the default AdventureWorks provided by Azure when creating your first DB). To connect to the Azure SQL, I have created an App (service principal) and granted it reader access to my Azure SQL DB. If I login to the Azure SQL DB from SSMS with this account, using Microsoft Entra Service Principal Authentication providing ClientId@TenantID for the Username and SecretValue as the password, I am able to login and SELECT from the tables. However, when I try to process the SSAS model, I get an error. For reference, below I have put the TMSL script that sets the DataSource part of the SSAS after deployment via YAML pipelines (variables are replaced when running). I think the issue lies in the "AuthenticationKind" value I have provided in the credential, but I can't figure out what to use. When I create the datasource like this and process, I get error: Failed to save modifications to the server. Error returned: '<ccon>Windows authentication has been disabled in the current context.</ccon>. I don't understand why since I am not using Windows authentication kind. Every other keyword I used ib the "AuthenticationKind" part returns error AuthenticationKind not supported. Any help on how to change this script would be useful. { "createOrReplace": { "object": { "database": "$(AAS_DATABASE)", "dataSource": "$(AZSQLDataSourceName)" }, "dataSource": { "type": "structured", "name": "$(AZSQLDataSourceName)", "connectionDetails": { "protocol": "tds", "address": { "server": "$(AZSQLServer)" }, "initialCatalog": "$(AZSQLDatabase)" }, "credential": { "AuthenticationKind": "ServiceAccount", "username": "$(AZSQL_CLIENT_ID)@$(AZSQL_TENANT_ID)", "password": "$(AZSQL_CLIENT_SECRET)" } } } }
Part 1: Power BI Service Connections to Azure Databricks with Private Networking
This blog was written in conjunction with Leo Furlong, Lead Solutions Architect at Databricks. Enhancing Security and Connectivity: Azure Databricks SQL, Unity Catalog, and Power BI Integration The combination of Azure Databricks SQL, Unity Catalog, and Power BI offers an unparalleled set of capabilities for modern data analytics. However, as organizations increasingly prioritize security, many Azure Databricks customers are deploying their Databricks workspace with private networking requirements which requires additional configuration for allowing connections from BI tools like Power BI. This blog post explores the options available for secure Azure Databricks deployments and how to maintain Power BI connectivity in these scenarios. Private Networking Options for Azure Databricks When deploying Azure Databricks with enhanced security, customers can choose from three main private networking configurations: Public Endpoint with an IP Access List for the Workspace: This option exposes a public endpoint for the Azure Databricks workspace but restricts access to specific IP ranges. Azure Databricks Private Link: Front-end private link provides fully private connectivity, routing all traffic through private endpoints. Hybrid Deployment: Combines front-end private link with a public endpoint protected by a Workspace IP Access List which is typically used for SaaS service connections. Connecting Power BI to a Private Azure Databricks Workspaces While private networking enhances security, it can require additional connection configurations from SaaS services like Power BI. Power BI offers two primary methods for secure connections to data sources with private networking: On-premises data gateway: an application that gets installed on a Virtual Machine that has a direct networking connection to the data source. It allows Power BI to connect to data sources that don’t allow public connections Virtual Network Data Gateway: a managed (virtual/serverless) data gateway that gets created and managed by the Power BI service. Connections work by allowing Power BI to delegate into a VNet for secure connectivity to the data source. While Power BI offers these two options, many customers prefer not to manage additional infrastructure or configurations required for these gateways. In such cases, Power BI can be allowed to access the private Azure Databricks workspace through the IP Access List. Implementing Power BI Connectivity via IP Access List To enable the Power BI Service connectivity to a private Azure Databricks workspace using an IP Access List: Obtain the Power BI Public IPs: Download the latest Azure IP Ranges and Service Tags file from the Microsoft Download Center. This file is updated weekly and contains IP ranges for various Azure services, including Power BI. Add Power BI IPs to Azure Databricks Workspace IP Access List: Extract the Power BI IP ranges from the downloaded file and add them to the Azure Databricks IP Access List using the API or SDK. Regular Updates: Since Power BI public IPs can change frequently, it's crucial to update the Workspace IP Access List regularly. This can be automated using a Databricks Job that periodically downloads the latest IP ranges and updates the Workspace IP Access List. The Job will need to be run by a Workspace Admin in order to set the configurations. You can run the Databricks Job as a Service Principal to make the updates. If you use the Databricks SDK from within a notebook in the Databricks Workspace, authentication is handled for you. The following sample code can be used to turn on your Workspace IP Access List which is more of a one-time operation. The Power BI IPs for IP Access List sample code can be used to refresh your Power BI IPs from a Databricks Workflow. Conclusion By leveraging IP Access Lists, organizations can maintain the security benefits of private Azure Databricks deployments while ensuring seamless connections from Power BI. This approach offers a balance between security and functionality with low maintenance overhead.Microsoft teams user analytics custom dates
Hi team, Just wondering if there is a way to get Teams analytics on a custom dates range? I know we can get trends currently for 7, 30, 90 or 180 days. But I would like to generate a report for a particular time period, for example: for the Month of December (31 days). Is there a way to action this ? Thanks
