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Window Functions in Snowflake
4.9(524)2k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate SQL course unlocks advanced problem-solving capabilities in Snowflake by mastering window functions. You will learn to move beyond standard aggregation by assigning row rankings, partitioning datasets, detecting outliers, and calculating rolling averages.
Who It's For
Data analysts and Snowflake developers who want to write more efficient SQL queries for complex sequence and partition analysis.
Key Takeaways
- Anatomy & Ranking: Differentiate window functions from standard aggregations using row numbers and ranking functions.
- Partitions & Ordering: Create ordered partitions to compare sequential records across dataset groups.
- Rolling Aggregations: Implement aggregate window functions to calculate moving averages and identify data outliers.
About this course
Unlock a slew of new problem-solving tools with the power of Snowflake window functions! In this course, you'll master the tools needed to solve problems like identify outliers in your data and calculate moving averages.
First, you'll differentiate between traditional aggregation functions and window functions. You'll nail down the anatomy of a window function by assigning row numbers and rankings to all records in a Snowflake query. Once you've gotten your feet under you, you'll pair these window functions with partitions. This will give you the power to created ordered groups of records, and compare sequential values.
You'll wrap up the course with aggregate window functions and rolling averages; two of the most handy applications of window functions for wrangling and analyzing data. When all is said and done, you'll have a whole new skillset that will supercharge your Snowflake queries!
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