Table relationships
Customers, orders, lines, products and payments are connected by their keys.
Applied SQL · Reporting and quality
SQL queries for reporting and data quality
Connect a business question to a readable query, produce verifiable metrics and detect anomalies before they are used in a report.
01
Business question
The need is translated into metrics, scope, granularity and calculation rules.
02
Readable query
Joins, filters and aggregations structure the data needed for analysis.
03
Verified result
Volumes, duplicates, missing values and calculation differences are checked.
04
Output
The validated result can feed a report, an extract or an operational control.
Business context
The data needed for reporting is often spread across several tables: customers, orders, products, order lines and payments. A useful query must connect these objects without losing the business definition of the metric.
Challenge
How can reliable and understandable analytical results be produced while identifying inconsistent data and query patterns that may degrade performance?
Approach
The prototype starts from a simple relational model and illustrates three uses directly related to reporting: calculating monthly KPIs, consolidating quality checks and writing a time filter compatible with index use.
Understandable model
Explicit keys and relationships to follow the path from customer to payment.
Readable calculations
Intermediate aggregations make the logic easier to review and adapt.
Visible quality
Rules return a name, an affected volume and a priority level.
Evidence-based performance
The execution plan helps verify table access instead of assuming a performance gain.
What the demo shows
Explore three T-SQL query examples
Each example connects a business question to a query and a fictitious result. The queries are not executed by this page: the output is simulated locally to explain the reasoning and control points.
Front-end demonstration: the code and results are fictitious examples displayed in the browser. No SQL server or external data is queried.
Query
T-SQLFictitious result
Pending
Choose an example and display the result to see the output and its associated control point.
Prototype deliverables
The screenshots document the original prototype built in SQL Server Management Studio. They illustrate the model, results, checks and a logical flow without claiming to represent a complete industrialised platform.
The model connects five entities and provides the basis for a monthly aggregation that can be used for reporting.
Monthly prototype view
Revenue, orders, average order value and active customers are grouped by month.
The example compares a filter that applies a function to the date with one that uses direct bounds. The plan must be reviewed on real data before concluding that there is a performance gain.
Before rewriting
Applying a function to the date column can limit efficient index use.
After rewriting
Direct bounds make the filter sargable. Forcing an index should still be avoided without measurement and justification.
A compact output consolidates the checks and ranks them by their potential impact on reporting.
Consolidated checks
Duplicates, missing customers, negative payments and orphan references are counted.
The diagram shows a logical separation between raw data, clean data, output and audit. It is an educational design, not active orchestration.
Logical zones
Staging, clean, mart and audit separate processing responsibilities.
Execution log
Statuses, timestamps, volumes and messages make successful or interrupted runs visible.
SQL Server
T-SQL
SSMS
Joins
Aggregations
Data quality
Business value
Explainable metrics
The business definition remains visible in joins, filters and aggregations.
More reliable reporting
Checks detect data that could distort an output.
Adaptable queries
A readable structure makes review, correction and changing requirements easier.
Easier diagnosis
The execution plan and log help locate a slowdown or failure.
From demo to enterprise use
In a real context, queries must be adapted to the schema, data volume, access rights and business rules of the organisation. Business validation and comparison with reference sources remain essential.
Separate environments
Server and database parameters distinguish development, testing and production.
Preserved query folding
With Power Query, filters and columns are pushed down to SQL while the steps remain translatable.
Controlled access
Read-only accounts access only the views and data needed for reporting.
Monitored checks
Volumes, anomalies and statuses are retained over time to support review and operations.
Query folding is the translation of Power Query steps into a SQL query executed by the source. Filtering early and avoiding a premature
Table.Buffer() helps preserve it. These choices must be verified in Power Query and with the generated plan because their effect depends on the source and transformations.
Going further
SQL can support operational reporting, quality control, ad hoc analysis or the preparation of a Power BI model, provided that each query is linked to a clear business definition.