Power BI workflow case study
Prepare cleaner SQL for Power BI without connecting AI to your database
This walkthrough shows how a Power BI user can move from database metadata to a reviewed SQL query using a safe schema replica and generated dummy data, while keeping production data and database credentials out of the AI workflow.
The goal
Prepare a focused SQL dataset that can be used with Power BI, with the right tables, columns, filters, and joins.
The challenge
A user may be working with an unfamiliar or wide schema and still needs to verify the query before it runs against live data.
The approach
Review schema metadata and relationships, generate SQL from a natural-language request, then test its logic against dummy rows.
Why this workflow matters
Power BI starts with the quality of the query behind it
Power BI can transform and model data, but the SQL used to retrieve that data still needs to join the correct tables, apply the intended filters, and return a useful result shape. Those decisions become harder when the database structure is unfamiliar or contains many tables and columns.
SQL Mocker creates a review step before the query reaches the database. Instead of giving AI direct access to production systems, the user supplies a safe copy of the database structure. The assistant can then work from actual table names, columns, keys, and confirmed relationships while the underlying rows remain in the user's environment.
The result is not an automatic Power BI connection. It is a controlled way to prepare and validate the SQL that will later be run in the user's database tool and used as a Power BI data source.
The demonstrated workflow
From database structure to a Power BI-ready query
01
Add the database structure
Extract or upload table names, column names, data types, keys, and relationship metadata. SQL Mocker does not need database credentials or a live connection.
02
Review the fields Power BI needs
Inspect the detected schema and keep the tables and output columns relevant to the report. Unused columns can be hidden from the active SQL context without being permanently deleted.
03
Confirm how the tables connect
Review primary keys, foreign keys, and suggested relationships before SQL is generated. This gives the assistant the context needed for reliable multi-table joins.
04
Describe the required dataset
Ask a question in natural language. In the walkthrough, the request brings together customers, orders, and regions to prepare a focused reporting dataset.
05
Preview the result with dummy data
Use generated sample rows to check joins, filters, selected columns, and the shape of the output before running the SQL against real data.
06
Review and use the SQL
Inspect the generated query, make any final adjustments, then copy it into your database tool and use the verified result as a Power BI data source.
Example request
Ask for the dataset, not the SQL syntax
Write a SQL query for Power BI that returns furniture order revenue by region from my existing customers, orders, and regions tables.
Because the schema and relationships have already been reviewed, SQL Mocker can use the real database structure when selecting fields and constructing joins.
SELECT
r.region_name,
COUNT(DISTINCT c.customer_id) AS customer_count,
COUNT(o.order_id) AS order_count,
SUM(o.amount) AS furniture_revenue
FROM regions AS r
INNER JOIN customers AS c
ON c.region_id = r.region_id
INNER JOIN orders AS o
ON o.customer_id = c.customer_id
WHERE o.product_category = 'Furniture'
GROUP BY r.region_name
ORDER BY furniture_revenue DESC;Real data stays put
SQL Mocker works from metadata and generated sample rows, not private production records or database credentials.
Assumptions stay visible
Tables, columns, keys, and relationships can be reviewed before the SQL is used.
The output is inspectable
Users can review the generated query and preview its result shape before moving it into the real reporting workflow.
What the workflow improves
- Fewer unnecessary fields in the dataset sent to Power BI.
- More visible join paths across unfamiliar tables.
- A safer way to inspect filters and output shape before using real rows.
- SQL tailored to the reviewed database structure instead of a generic example.
- A clear handoff from AI-assisted drafting to human review and database execution.
Explore the workflow
Prepare schema-aware SQL for your next Power BI dataset
Review the focused Power BI use case or open SQL Mocker and start with your own database structure.