Updated

Updated · KDnuggets · Jun 19

Bala Priya C Outlines 7 SQL Patterns for Data Analysis Using 36 SaaS Transactions

Updated

Updated · KDnuggets · Jun 19

Bala Priya C Outlines 7 SQL Patterns for Data Analysis Using 36 SaaS Transactions

2 articles · Updated · KDnuggets · Jun 19

Seven advanced SQL patterns anchor Bala Priya C’s guide, which uses a 36-transaction SaaS dataset across 7 customers to move beyond basic SELECT, WHERE and GROUP BY analysis.
Window functions drive much of the tutorial: LAG() measures gaps between transactions, ROW_NUMBER() picks each customer’s top row, NTILE(4) builds spend quartiles, and a 3-month rolling average smooths monthly revenue.
The guide also shows a self-join to detect plan upgrades, FILTER to calculate completed revenue, refunds and failed counts in one monthly query, and a streak method to find consecutive active months.
Priya frames the patterns as standard SQL tools for retention analysis, upgrade tracking and revenue reporting, arguing they can replace many multi-step transformations often pushed into Python.

Sources

Will AI-powered BI tools make mastering these complex SQL patterns an obsolete skill for data scientists?

How do these SQL patterns impact query costs and performance on modern cloud data warehouse platforms?

When does moving complex data logic from Python back into SQL become a performance anti-pattern?

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