Extracting real business intelligence from enterprise data means moving past spreadsheet filters and manual exports. To answer complex, cross-functional questions at scale, analysts and report developers need to query large relational databases directly, writing clean, high-performance code that holds up under heavy transactional volume.
Grounded in official Microsoft Learn documentation and delivered inside Azure SQL Database, this course builds that fluency module by module. You will start by consolidating disparate enterprise tables with complex join logic, including self-joins to flatten organisational hierarchies and cross-joins for combinatorial matrices, while avoiding null and data-exclusion traps. You will then move into diagnostic analytics, using multi-level aggregations and correlated subqueries to isolate regional trends and surface hidden outliers across multi-million row datasets.
In the final modules you will compute time-series metrics with T-SQL window functions, calculating moving averages, running totals, and performance rankings without collapsing row detail, then refactor deeply nested legacy queries into clean, modular CTEs. You will learn to read execution plans and turn costly table scans into fast index seeks. You leave with a portfolio-ready master script, the Global Enterprise Operations Analytics Script, that you can adapt to your own organisation.
This course is for data analysts, report developers, software engineers, and analytically minded business professionals who already know basic SQL and want to scale into advanced, high-performance querying over cloud-scale databases.
