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PopularAnalysts often struggle when standard SQL aggregation isn't enough to provide context for individual records. You can easily calculate total sales for a region, but comparing each customer's purchase against that region's total, or calculating a running sum over time, usually requires complex subqueries or self-joins that are inefficient and difficult to maintain. The core challenge is performing calculations across related rows without collapsing the detail of the dataset.
Without the right tools, generating critical reports—like ranking top performers, calculating month-over-month growth, or finding the previous period's value—becomes a multi-step, error-prone process. This slows down time-to-insight and limits the depth of business analysis. This masterclass solves that by introducing Window Functions, which unlock powerful, row-by-row analytical capabilities within a single query. You'll follow Rajiv, an analyst at GlobalMart, as he uses a structured scenario and hands-on examples to fulfill complex, real-world reporting requests from his manager, Robert.
What You'll Learn:
Foundational Windowing & Aggregation
Calculate a sub-category's % contribution to its respective category's total product count (Report 1).
Understand and implement aggregate functions like SUM() within the OVER(PARTITION BY...) clause to create context-aware totals.
Ranking and Filtering Analysis
Assign accurate ranks to vendors based on their delivery volume within different geographic regions (Report 2).
Implement the RANK() function using the PARTITION BY and ORDER BY clauses to segment and sort data.
Combine Window Functions with CTEs (Common Table Expressions) to correctly filter a result set based on rank (e.g., reporting only the top 3 customers by sales in each region) (Report 3).
Differentiate between ROW_NUMBER(), RANK(), and DENSE_RANK() and know when to apply each for position-aware vs. non-position-aware ranking.
Advanced Time-Series Comparisons
Analyze time-dependent data using offset functions to compare current values with previous or future values (MoM % change report).
Implement LAG() to retrieve the preceding month's order count for Month on Month % change calculation.
Implement LEAD() to retrieve the subsequent value, a common requirement for forecasting preparation.
By the end, you'll understand Window Functions—so you can perform complex ranking and segmentation, calculate running totals and contributions, and conduct powerful time-series analysis entirely in SQL. Test your knowledge throughout with scenario-based questions.