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- [Microsoft Fabric Overview](https://learn.microsoft.com/en-us/fabric/fundamentals/microsoft-fabric-overview)
- [OneLake Overview](https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview)
- [Mirroring in Fabric](https://learn.microsoft.com/en-us/fabric/mirroring/overview)
- [OneLake Shortcuts](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts)
- [OneLake Explorer](https://learn.microsoft.com/en-us/fabric/onelake/onelake-file-explorer)
- [Fabric Data Agent](https://learn.microsoft.com/en-us/fabric/data-science/concept-data-agent)
- [Fabric Data Agent in Microsoft Copilot Studio](https://learn.microsoft.com/en-us/fabric/data-science/data-agent-microsoft-copilot-studio)
| +| *Preparation: Familiarize yourself with Microsoft Fabric, Data Agents, Microsoft 365 Copilot, and Azure databases.* | **Microsoft Fabric and mirroring resources:**
- [Microsoft Fabric Overview](https://learn.microsoft.com/en-us/fabric/fundamentals/microsoft-fabric-overview)
- [OneLake Overview](https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview)
- [Mirroring in Fabric](https://learn.microsoft.com/en-us/fabric/mirroring/overview)
- [OneLake Shortcuts](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts)
- [OneLake Explorer](https://learn.microsoft.com/en-us/fabric/onelake/onelake-file-explorer)
- [Fabric Data Agent](https://learn.microsoft.com/en-us/fabric/data-science/concept-data-agent)
- [Fabric Data Agent in Microsoft Copilot Studio](https://learn.microsoft.com/en-us/fabric/data-science/data-agent-microsoft-copilot-studio)
| # Lab Overview In this walkthrough, you will set up database mirroring from Azure SQL Managed Instance to Microsoft Fabric. This process enables you to replicate operational data into Fabric’s OneLake for real-time analytics, reporting, and AI—without affecting source database performance. -By using System Assigned Managed Identity (SAMI) for secure access, you will configure a mirrored database in Fabric, and start the mirroring process. You will learn to monitor synchronization and repeat the setup for additional databases. +You will configure a mirrored database in Fabric, start the mirroring process, and monitor synchronization. You will then repeat the setup for an additional database. -And, you will have a synchronized, mirrored database in Microsoft Fabric that supports up-to-date analytics and downstream workloads. +At the end of the lab, you will have synchronized mirrored databases in Microsoft Fabric that support up-to-date analytics and downstream workloads. -By completing this lab, you will start by integrating mirrored data from Azure SQL Managed Instance and external CSV files into a single Lakehouse, enabling centralized access and unified analytics. You will then build and optimize a Semantic Model to support accurate reporting, effective time-based analysis, and high query performance. +By completing this lab, you will integrate mirrored data from Azure SQL Managed Instance and an external CSV file into a single Lakehouse, enabling centralized access and unified analytics. # 1. Create a Mirrored Azure SQL Managed Instance Database @@ -47,14 +47,21 @@ In this task, you will enable managed identity authentication on your Azure SQL NarrativeNotes - Go to Microsoft Fabric. On the Fabric Home page, select Fabric (bottom-left) (1). Next Select Workspace (2) and choose your predeployed workspace. (3) - + Go to Microsoft Fabric and sign in with the credentials provided for your lab environment. On the Fabric Home page, select Fabric in the bottom-left corner (1), select Workspaces (2), and open your predeployed workspace (3). + Use the workspace assigned to you for this lab. - In your workspace, select New item (2). In the New item window, search for Mirror (3) to display all mirroring options and Click Mirrored Azure SQL Managed Instance (4) to start creating a mirrored database from your SQL Managed Instance. + Troubleshooting: After signing in for the first time, check the Workspaces list. If your predeployed workspace is not visible, perform a hard refresh using Ctrl+Shift+R in Edge or Chrome, then open Workspaces again. + The workspace list may initially show only My workspace. A hard refresh reloads the list so the assigned workspace becomes visible. + + + + + + In your workspace, select New item (2). In the New item window, search for Mirror (3), then select Mirrored Azure SQL Managed Instance (4) to start creating a mirrored database. @@ -68,22 +75,42 @@ In this task, you will enable managed identity authentication on your Azure SQL - In the Server (1) field, paste the Azure SQL Managed Instance server name:sqlhackmi-z5v5uebsfrojm.8b4846304eec.database.windows.net so that Microsoft Fabric knows exactly which database to connect to for mirroring. This step directs Fabric to the correct SQL Managed Instance, ensuring secure authentication and accurate data synchronization from your operational system to the analytics environment. - Copy and paste directly. + In the Server (1) field, paste the Azure SQL Managed Instance server name: sqlhackmi-z5v5uebsfrojm.8b4846304eec.database.windows.net. + Copy and paste the value exactly. In the Database (2) field, enter the source database name: TailspinToys_User### - Replace ### with your  ttyd user postfix + Replace ### with your assigned ttyd user postfix. - Select a Data gateway (3) after entering the server and database to enable secure data transfer between your on-premises or network-restricted Azure SQL Managed Instance and Microsoft Fabric. The data gateway acts as a bridge, allowing Fabric to access and replicate data from the source database while maintaining network security and compliance requirements. - + In the Connection name field, enter a short, unique name such as TailspinToys_User###. Use the same user postfix as the database name so that you can easily identify the connection later. + Do not leave the automatically generated server name as the connection name. + + + + + + +
+ Troubleshooting: connection name is too long or already exists
+ Fabric may show a warning that the automatically generated connection name is too long and will be truncated. Replace it with a short name such as TailspinToys_User### before selecting Connect.
+ If the shortened name is already in use, choose another short, descriptive name. Keep the Server, Database, Data gateway, Authentication kind, Username, and Password values unchanged. +
+ + Connection names are workspace-scoped and must be unique. + + + + + + Under Data gateway (3), select the preconfigured gateway for the lab. + If no gateway is listed, contact the lab facilitator before continuing. @@ -103,21 +130,23 @@ In this task, you will enable managed identity authentication on your Azure SQL - Once you are successfully connected, on the Choose data screen, review the list of available tables. Select the tables you want to replicate (or select Select all if required for the lab). Click Connect to proceed. - This may take a few minutes to connect. + + + On the Choose data screen, select Select all to replicate all available tables, then click Connect. + This may take a few minutes. - In the Destination screen, review the Name of the mirrored database. Verify that Azure SQL Database Managed Instance is shown as the source. Click Create mirrored database to start the mirroring process. + On the Destination screen, review the mirrored database Name and verify that Azure SQL Managed Instance is shown as the source. Select Create mirrored database to start the mirroring process. - Please remain on this page and avoid refreshing while the system completes the database mirroring process. + Remain on this page and do not refresh it while Fabric completes the database mirroring setup. This may take a few minutes to complete. @@ -139,17 +168,16 @@ In this task, you will initiate the mirroring process between your Azure SQL Man NarrativeNotes - The Monitor Replication screen will allow you to mirror all data in the database by default. - After 2-5 minutes, select Monitor Replication to see the replication status. + Open the Monitor Replication screen to view the replication status and confirm that the selected tables are being synchronized. + Wait 2–5 minutes, then select Monitor Replication. - Replicating Status:
Running: Replication is currently running, bringing snapshot and change data into OneLake.
Running with warning: Replication is running with transient errors.
Stopping/Stopped: Replication is stopped.
Error: Fatal error in replication that can't be recovered. - If you don't see the tables and corresponding replication status, wait a few seconds and refresh the pane. + Replication status:
Running: Replication is bringing snapshot and change data into OneLake.
Running with warning: Replication is running with transient errors.
Stopping/Stopped: Replication is not running.
Error: Fabric reported a replication failure that requires investigation. + If the tables or status are not visible, wait a few seconds and refresh the pane. - @@ -167,7 +195,7 @@ In this task, you will repeat the setup and monitoring procedures for additional NarrativeNotes - In your Fabric workspace, select New item (2). In the New item window, search for Mirror (3) to display all mirroring options and Click Mirrored Azure SQL Managed Instance (4) to start creating a mirrored database from your SQL Managed Instance. + Repeat the mirroring workflow in your Fabric workspace: select New item (2), search for Mirror (3), and select Mirrored Azure SQL Managed Instance (4). @@ -181,38 +209,31 @@ In this task, you will repeat the setup and monitoring procedures for additional - In the Server (1) field, paste the Azure SQL Managed Instance server name: sqlhackmi-z5v5uebsfrojm.8b4846304eec.database.windows.net - + In the Server (1) field, paste sqlhackmi-z5v5uebsfrojm.8b4846304eec.database.windows.net. + Use the same SQL Managed Instance server as in the first mirroring setup. In the Database (2) field, enter the source database name: TailspinToysFeedback_User### - Replace ### with your  ttyd user postfix + Replace ### with your assigned ttyd user postfix. - Select existing Data gateway (3) - - - - + In the Connection name field, enter a short, unique name such as TailspinToysFeedback_User###. Use the same user postfix as the database name. + Do not leave the automatically generated server name as the connection name. - For Authentication kind (4) , select Basic. In the Username (5) field, enter the SQL login username: demouser and in the Password (6) field, enter the corresponding password: Demo@pass1234567.
Ensure Use encrypted connection is checked. Click Connect to validate the connection and continue. - + - - - - +
Troubleshooting
- If you see the error "The specified connection name already exists. Try choosing a different name.", go back to the connection name and enter a new unique connection name at the end (for example: TailspinToysFeedback_User###_2).
+ If you see the error "The specified connection name already exists. Try choosing a different name.", go back to the connection name and choose another short, descriptive name.
Keep the same Server, Database, Data gateway, Authentication kind, Username, and Password values, then click Connect again.
@@ -223,8 +244,22 @@ In this task, you will repeat the setup and monitoring procedures for additional - Select all tables from the database. - This may take a few minutes to complete. + Under Data gateway (3), select the preconfigured gateway for the lab. + If no gateway is listed, contact the lab facilitator before continuing. + + + + + + For Authentication kind (4), select Basic. Enter demouser in Username (5) and Demo@pass1234567 in Password (6). Ensure Use encrypted connection is checked, then select Connect. + This validates the SQL Managed Instance connection. + + + + + + On the Choose data screen, select all tables, then click Connect. + This may take a few minutes. @@ -248,18 +283,20 @@ In this task, you will integrate operational data mirrored from Azure SQL Manage NarrativeNotes -Recap: You mirrored two databases in Challenge 1.
You need to create a Lakehouse after mirroring two databases so you can combine and centralize data from multiple sources. -Open your Workspace and Click New Item (1). In the search bar, search for Lakehouse (2) and select the Lakehouse (3). +Recap: You mirrored two databases in the previous steps. Now create a Lakehouse so you can combine and centralize data from multiple sources. +Open your workspace, select New item (1), search for Lakehouse (2), and select Lakehouse (3). -You need to enter lakehouse name and create it.Example Name: TailspinToysAnalytics and Leave Lakehouse schemas enabled. +Enter a name for the Lakehouse and select Create.Use TailspinToysAnalytics as the name and leave Lakehouse schemas enabled. -Open your new Lakehouse. Within the Tables folder navigate to dbo schema. By clicking the ellipsis (...) create shortcuts to the required tables (mirrored databases). Select New table shortcut +Open your new Lakehouse. In the Tables folder, open the dbo schema, select the ellipsis (...), and choose New table shortcut.Create shortcuts to the required tables from the mirrored databases. In the New shortcut window, under Internal sources, select Microsoft OneLake.This option allows you to create shortcuts from mirrored databases stored in OneLake. -In the Select a data source type screen, locate the mirrored database TailspinToys_User###. Select TailspinToys_User### created in Challenge 1. +On the Select a data source type screen, locate the mirrored database TailspinToys_User###.Select the TailspinToys_User### database created in this challenge. -In the new shortcut screen, expand tables → dbo. Select alle tables except zzVersion (1). Click Next (2) to proceed.Important: Do NOT select zzVersion (this table is not required). +After the Data Lake Storage connection is validated, the Connection method dialog opens. Leave Passthrough identity selected. This uses each user's own permissions and is the recommended option for this lab. Click Connect to continue.Use Delegated identity only when the lab explicitly requires a shared credential for all users. + +In the New shortcut screen, expand Tables → dbo. Select all tables except zzVersion (1), then click Next (2).Important: Do not select zzVersion; it is not required for this lab. Review the Summary screen and confirm the selected tables. Verify that the shortcut location is your current Lakehouse. Click Create to create the shortcuts. @@ -267,7 +304,7 @@ In this task, you will integrate operational data mirrored from Azure SQL Manage - +
Repeat the process by creating another New table shortcut.
- + This time select the mirrored database TailspinToysFeedback_User###. Click Next to continue. -Expand Tables → dbo. Select all tables except Customer (1). Click Next (2) to proceed.Important: Do NOT select the Customer table, as it already exists from the first database. +Expand Tables → dbo. Select all tables except Customer (1), then click Next (2).Important: Do not select Customer; it already exists from the first mirrored database. Review the Summary screen and confirm the selected tables. Verify the shortcut destination is the same Lakehouse. Click Create to finalize the shortcuts. -Next you create a Shortcut from a file. Therefore click the ellipsis (...) next to the files folder in the explorer pane. This let's you easily integrate external data, such as CSV files, alongside your mirrored databases within the same Lakehouse for centralized analysis. +Next, create a file shortcut. Select the ellipsis (...) next to the Files folder in the Explorer pane.This lets you integrate the external CSV data with the mirrored databases in the same Lakehouse. Select New shortcut (2) to start creating a shortcut to an external data source. -In the New shortcut window, choose the source type and Under External sources, select Azure Data Lake Storage Gen2 so you can directly link and access files stored in your organization's data lake, enabling seamless integration of external datasets with your Lakehouse environment for unified analytics. +In the New shortcut window, under External sources, select Azure Data Lake Storage Gen2.This connects the Lakehouse to the external files used in the lab. -Select New connection and Paste DataLake Storage URL: https://employeedata####.dfs.core.windows.net/ (1). Replace #### in the URL with the current day and month in DDMM format (for example, 1206 for June 12). Click Next (2) to continue.This ensures you are establishing a secure and direct connection to your organization’s Data Lake for accessing external files. +Select New connection and paste https://employeedata1309.dfs.core.windows.net/ (1). Click Next (2) to continue.Confirm the deployment date with the lab facilitator or deployment output if needed. Select the folder with your user number (1). Click Next (2) to continue.Selecting your user-specific folder ensures you only access and work with the files intended for your lab activities. -Review the selected folder (1) and Click Skip(2) as you do not need to apply any transformation before creating the shortcut. +Review the selected folder (1) and click Skip (2). No transformation is required before creating the shortcut. Click Create to create the shortcut in your Lakehouse. -Check shortcut creationIf the shortcut does not appear immediately, click the three dots next to Files and select Refresh. +Verify that the shortcut was created.If it does not appear immediately, select the ellipsis (...) next to Files and choose Refresh. -Click container### and verify that the CSV file is visible in this container.Replace ### with your user number prefix/postfix. +Open container### and verify that the CSV file is visible.Replace ### with the user number shown in your environment. -Expand the Files folder. Click the Shortcut folder with your user name. Locate the CSV file and click the three dots (⋯) (e.g. employees_user_data.csv). +Expand the Files folder, open the shortcut folder with your user name, locate the CSV file (for example, employees_user_data.csv), and select its ellipsis (...). -Select Load to Tables (1) from the context menu and Choose New table (2). +From the context menu, select Load to Tables (1), then choose New table (2). Change the table name to employees (1). Set the separator to , (comma) (2). Review the settings, then click Load (3).CSV files use commas to separate values, so selecting a comma ensures data loads into the correct columns. @@ -318,7 +355,7 @@ In this task, you will integrate operational data mirrored from Azure SQL Manage In this lab, you have accomplished the following: -- Created a mirrored Azure SQL Managed Instance database to ensure high availability and data replication. +- Created mirrored Azure SQL Managed Instance databases to replicate operational data into OneLake. - Started the mirroring process and monitored Fabric mirroring to ensure successful synchronization and data consistency. - Built a unified Lakehouse in Microsoft Fabric OneLake by combining mirrored Azure SQL databases with external CSV files, establishing a centralized and analytics-ready data foundation. diff --git a/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-02/solution-02.md b/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-02/solution-02.md index 333dbc60a..4e65bda0b 100644 --- a/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-02/solution-02.md +++ b/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-02/solution-02.md @@ -96,8 +96,12 @@ In this task, you will optimize the semantic model by creating the required rela Create Sales[ProductID] -> Product[ProductID].This is the core product join for sales analytics. -Create employees[SalesOfficeID] -> SalesOffice[SalesOfficeID].Enables organizational analysis by office and region. Fabric may auto-select Email first because it is a common column, so make sure you choose SalesOfficeID before saving the relationship. +Create the employees[SalesOfficeID] -> SalesOffice[SalesOfficeID] relationship. If SalesOfficeID is too far to the right to select while creating the relationship, first select a visible column such as Email, then correct the relationship before continuing.Fabric may initially select Email or another visible column. The relationship is correct only when SalesOfficeID is selected on both sides. +Option 1 - Update the relationship in Model view: select the SalesOfficeID field in the employees table and connect it to SalesOfficeID in the SalesOffice table. Confirm that the relationship line connects the correct fields.This option is useful when you prefer to manage the relationship visually from the model diagram. + +Option 2 - Update the relationship in the Properties pane: select the relationship line, then use the Properties pane to set the first table to employees with column SalesOfficeID, and the second table to SalesOffice with column SalesOfficeID. Apply the changes.This option is useful when the field is difficult to select from the relationship dialog because it is not visible without horizontal scrolling. + Review all listed relationships and confirm they are Active, then close the dialog.Quick validation to ensure no required join is missing. Verify the model diagram now shows connected relationship lines.The model should now reflect the intended star / multi-fact shape. @@ -154,7 +158,7 @@ In this step, use the semantic model to automatically generate the first report NarrativeNotes Navigate back to the workspace by clicking the workspace icon in the left navigation. -Open the semantic model and select Explore from the top menu, then click Auto-create a report.This generates a first draft of the report automatically based on the available fields and data patterns. +In the workspace, locate the semantic model and click its More options (...) menu (1). Select Auto-create report (2).The Auto-create report option is available from the semantic model's context menu. It generates a first draft of the report automatically based on the available fields and data patterns. Review the generated Quick summary page and check the suggested visuals.You can also adjust the field selection in the Your data pane on the right if needed. @@ -172,8 +176,10 @@ In this step, use prompts or Copilot suggestions to improve the auto-generated r NarrativeNotes Switch to Edit mode from the report menu and confirm with Continue.Use this mode to manually refine and optimize the generated report. - First click the Copilot icon (1). Then a sidebar opens to the left where you can add your prompt (2) to add something on the report page, like: Provide a detailed, insight-focused overview of "Sum of Quantity by RegionName" visual's data.
Use Copilot prompts to refine the auto-generated report with better visuals, clearer wording, or additional insights.This screenshot shows an example of how Power BI Copilot can help improve the report. - +Click the Copilot icon. In the Copilot pane, enter this prompt: Generate a business insights report for this visual. Focus on major findings, anomalies, opportunities, and business impact instead of describing the chart.Use this prompt when you want Copilot to analyze an existing visual and summarize its business meaning. Review the generated content because AI-generated output may be inaccurate. + +To enhance the overall report, enter this prompt in the Copilot pane: Enhance this report by adding visuals and summaries that highlight key insights, trends, outliers, and business opportunities. Optimize the layout for executive decision-making.This prompt asks Copilot to add KPI cards and narrative summaries, including an overall quantity card and a Midwest order quantity card. Review the proposed changes before applying them. + ## 6. Manually Fine-Tune the Report @@ -201,9 +207,9 @@ In this task, you will simplify the data schema and prepare the model for AI and NarrativeNotes -Open your workspace and open the semantic model in Microsoft Fabric. +In the workspace, locate the semantic model you want to prepare and click its row. Confirm that the item is a Semantic model, then use the available actions to open it.The highlighted row identifies the semantic model to prepare. Do not select the related report or SQL analytics endpoint. -From the top menu, select Prep data for AI. +Open the semantic model. If necessary, switch to Editing mode (1), then select Prep data for AI (2) from the top menu.The Prep data for AI command is available when the semantic model is open in Editing mode. In the Prep data for AI dialog, review and configure the steps: simplify the data schema, verified answers, and AI instructions. @@ -217,7 +223,7 @@ In this task, you will simplify the data schema and prepare the model for AI and Finalize the schema cleanup.State: uncheck RegionID and StateID -After reviewing your selections, click Apply. +After reviewing your selections, click Apply. When the button becomes unavailable, the selection has been saved; continue immediately to the next step.Important: Do not wait for a loading or completion message after selecting Apply. The wizard does not automatically advance. @@ -245,7 +251,7 @@ In this task, you will add business-focused AI instructions so Copilot and other NarrativeNotes -In the left navigation, select Add AI instructions (preview). In the text box, add clear, business-focused instructions, then click Apply. +In the left navigation, select Add AI instructions (preview). In the text box, add clear, business-focused instructions, then click Apply. Once Apply becomes unavailable, continue to the next step.Important: The wizard does not show a loading indicator or move forward automatically after applying the instructions. You can select the next step immediately. See the example section below for a sample instruction set. diff --git a/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-03/solution-03.md b/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-03/solution-03.md index d90fe6d0c..d48f84897 100644 --- a/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-03/solution-03.md +++ b/03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-03/solution-03.md @@ -567,6 +567,7 @@ In this task, you will publish and open the Data Agent so it can be used for tes NarrativeNotes +Before publishing, review the Data Agent description and make sure it clearly explains the agent's purpose, data scope, and the types of questions it can answer.Important for Microsoft 365 Copilot: A missing or vague description can cause Copilot to use alternative sources, such as general knowledge or web search, instead of this agent. A specific description helps Copilot identify and use the correct agent. In the Data Agent editor, locate the Publish button in the top menu (1). Enable Also publish to the Agent Store in Microsoft 365 Copilot by turning the toggle On (2). Click Publish (3) to complete the publishing process.Once published, the agent moves from Draft to Published status. Open the Notifications panel from the top right corner (1). Verify the message "Successfully published data agent" appears (2).This confirms the agent was published without errors.