Lessons Learned #537: Copilot Prompts for Troubleshooting on Azure SQL Database
We had the opportunity to share our experience in several community sessions how SSMS Copilot can help across multiple phases of troubleshooting. In this article, I would like to share a set of prompts we found in those sessions and show how to apply them to an example query. During a performance incident, we captured the following query, generated by PowerBI. SELECT TOP (1000001) * FROM ( SELECT [t2].[Fiscal Month Label] AS [c38], SUM([t5].[Total Excluding Tax]) AS [a0], SUM([t5].[Total Including Tax]) AS [a1] FROM ( SELECT [$Table].[Sale Key] as [Sale Key], [$Table].[City Key] as [City Key], [$Table].[Customer Key] as [Customer Key], [$Table].[Bill To Customer Key] as [Bill To Customer Key], [$Table].[Stock Item Key] as [Stock Item Key], [$Table].[Invoice Date Key] as [Invoice Date Key], [$Table].[Delivery Date Key] as [Delivery Date Key], [$Table].[Salesperson Key] as [Salesperson Key], [$Table].[WWI Invoice ID] as [WWI Invoice ID], [$Table].[Description] as [Description], [$Table].[Package] as [Package], [$Table].[Quantity] as [Quantity], [$Table].[Unit Price] as [Unit Price], [$Table].[Tax Rate] as [Tax Rate], [$Table].[Total Excluding Tax] as [Total Excluding Tax], [$Table].[Tax Amount] as [Tax Amount], [$Table].[Profit] as [Profit], [$Table].[Total Including Tax] as [Total Including Tax], [$Table].[Total Dry Items] as [Total Dry Items], [$Table].[Total Chiller Items] as [Total Chiller Items], [$Table].[Lineage Key] as [Lineage Key] FROM [Fact].[Sale] as [$Table] ) AS [t5] INNER JOIN ( SELECT [$Table].[Date] as [Date], [$Table].[Day Number] as [Day Number], [$Table].[Day] as [Day], [$Table].[Month] as [Month], [$Table].[Short Month] as [Short Month], [$Table].[Calendar Month Number] as [Calendar Month Number], [$Table].[Calendar Month Label] as [Calendar Month Label], [$Table].[Calendar Year] as [Calendar Year], [$Table].[Calendar Year Label] as [Calendar Year Label], [$Table].[Fiscal Month Number] as [Fiscal Month Number], [$Table].[Fiscal Month Label] as [Fiscal Month Label], [$Table].[Fiscal Year] as [Fiscal Year], [$Table].[Fiscal Year Label] as [Fiscal Year Label], [$Table].[ISO Week Number] as [ISO Week Number] FROM [Dimension].[Date] as [$Table] ) AS [t2] ON [t5].[Delivery Date Key] = [t2].[Date] GROUP BY [t2].[Fiscal Month Label] ) AS [MainTable] WHERE ( NOT([a0] IS NULL) OR NOT([a1] IS NULL) ) I structure the investigation in three areas: Analysis – understand the data model, sizes, and relationships. List all tables in the 'Fact' and 'Dimension' schemas with space usage in MB and number of rows. The name of the tables and their relations among them. Please, provide a textual representation for all relations. List all foreign key relationships between tables in the 'Fact' and 'Dimension' schemas, showing the cardinality and referenced columns. Could you please let me know what is the meaning of every table? Describe all schemas in this database, listing the number of tables and views per schema. Create a textual data model (ER-style) representation showing how all Fact and Dimension tables are connected. Maintenance Plan Check – verify statistics freshness, index health/fragmentation, partition layout, and data quality. List all statistics in the database that have not been updated in the last 7 days, showing table name, number of rows, and last update date. List all indexes in the database with fragmentation higher than 30%, including table name, index name, and page count. Please, provide the T-SQL to rebuild all indexes in ONLINE mode and UPDATE STATISTICS for all tables that are automatic statistics. Check for fact table rows that reference dimension keys which no longer exist (broken foreign key integrity). Find queries that perform table scans on large tables where no indexes are used, based on recent execution plans. Performance Improvements – simplify/reshape the query and consider indexed views, columnstore, partitioning, and missing indexes. In this part, I would like to spend more time about these prompts, for example the following ones, help me to understand the performance issue, simplify the query text and also, explains what the query is doing. Identify the longest-running query in the last 24 hours provide the full text of the query Please simplify the query Explain me the query Explain in plain language what the following SQL query does, including the purpose of each subquery and the final WHERE clause. Show a histogram of data distribution for key columns used in joins or filters, such as SaleDate, ProductCategory, or Region. Finally, using this prompt I could find a lot of useful information how to improve the execution of this query: Analyze the following SQL query and provide a detailed performance review tailored for Azure SQL Database Hyperscale and Power BI DirectQuery scenarios. For each recommendation, estimate the potential performance improvement as a percentage (e.g. query runtime reduction, I/O savings, etc.). 1. Could this query benefit from a schemabound indexed view or a materialized view? Estimate the performance gain if implemented. 2. Is there any missing index on the involved tables that would improve join or filter efficiency? Include the suggested index definition and expected benefit. 3. Would using a clustered or nonclustered columnstore index on the main fact table improve performance? Estimate the potential gain in query time or storage. 4. Could partitioning the fact table improve performance by enabling partition elimination? If so, suggest the partition key and scheme, and estimate improvement. 5. Are current statistics sufficient for optimal execution plans? Recommend updates if needed and estimate impact. 6. Does this query preserve query folding when used with Power BI DirectQuery? If not, identify what breaks folding and suggest how to fix it. 7. Recommend any query rewrites or schema redesigns, along with estimated performance improvements for each. I got a lot of improvements suggestions about it: Evaluated a schemabound indexed view that pre‑aggregates by month (see Reference Implementations), then pointed Power BI to the view. Ensured clustered columnstore on Fact.Sale; considered a targeted rowstore NCI on [Delivery Date Key] INCLUDE ([Total Excluding Tax], [Total Including Tax]) when columnstore alone wasn’t sufficient. Verified statistics freshness on join/aggregate columns and enabled incremental stats for partitions. Checked partitioning by date to leverage elimination for common slicers.
M365 Roadmap Management
Wondering if others have tips & tricks on how they stay up to date with the https://www.microsoft.com/en-us/microsoft-365/roadmap? I find it tedious to stay on top of all the features being announced, switching launch phases, moving target dates, and so on. I was hoping to use the RSS feed in a PowerBI Dashboard or find a solution in Planner similar to syncing the Admin Centre messages, and after some quick searches online could not find an example of anyone doing something similar. I was hoping any members of this community may be able to shed some light on how they approach the roadmap site and what tools if any they use to manage the constant influx of information? TIA!
Mine your Azure backup data, it could save you 💰💡
Your data has a story to tell. Mine it, decipher it, and turn it into actionable outcomes. 📊🔍 Azure backups can become orphaned in several ways (I'll dive into that in a future post). But here’s a key point: orphaned doesn’t always mean useless, hence the word “Potential” in the title of my Power BI report. Each workload needs to be assessed individually. If a backup is no longer needed, you might be paying for it - unnecessarily and unknowingly. 🕵️♂️💸 To uncover these hidden costs, I combined data from the Azure Business Continuity Center with a PowerShell script I wrote to extract LastBackupTime and other metadata. This forms the foundation of my report, helping visualize and track backup usage over time. This approach helped me identify forgotten one-time backups, VMs deleted without stopping the backup, workloads excluded due to policy changes, and backups left behind after resource migrations. If you delete unneeded backups and have soft-delete enabled, the backup size drops to zero and Azure stops charging for it. ✅🧹 💡 Do your Azure backups have their own untold story to tell? 📸 Here's a snapshot of my report that helped me uncover these insights 👇
Data-driven Analytics for Responsible Business Solutions, a Power BI introduction course:
Want to gain inside on how students at Radboud University are introduced, in a praticle manner, to Power BI? Check out our learning process and final project. For a summary of our final solution watch our Video Blog and stick around till the end for some "wise words"!
Microsoft PowerBI Template and Project Web App
Hello all! Got the PowerBI Template up and running and enjoying it. However, I have noticed that for some reason, my projects that are showing up correctly, Example, Project 1, is showing the right people who are tasked in Project 1, however the taskings are showing them working on another project, which is and has been inactive over a year and not showing the new correct taskings. Anyone have ideas that can help there?
Seeking On-Premise Power BI P1 Capacity (Academic) or Suitable Alternative for Government Customer
Hello everyone, We are working with a government agency customer who requires Power BI P1 Capacity (On-Premise) with Academic classification. Unfortunately, we’ve been informed by Microsoft that this product has been discontinued and is now only available through Azure. However, due to strict data security policies, our customer is unable to use Azure as they cannot send data to external servers. We are looking for guidance on the following: Is there a direct successor to the Power BI P1 Capacity (On-Premise) with Academic classification that would meet these requirements? Are there any similar on-premise solutions for Power BI that could work for this customer? Any suggestions on how we can provide a suitable solution would be greatly appreciated! Thanks in advance for your assistance!
