Open the window to a world of possibilities with Snowflake window functions! You'll get the ball rolling by differentiating window functions from traditional functions.  Then, you'll learn how to provide a row number and ranking for each record in a query. Once you've nailed down the basics, you'll put the "window" in window functions, using PARTITION BY. You'll explore how to find and use the first and last value of a certain window before wrapping up with a sneak peek into aggregation functions.

Assigning row numbers

Building a Window Function

Building a window function

Adding row numbers to charging sessions

Riding the heat wave

Ranking window functions

Was that a fast charge?

Comparing energy consumed

Partitioning data in a window function

Follow the money!

Digging into charging costs

Window Functions

Time to crank it up! In this chapter, you’ll take ranking functions to the next level. You’ll start with a variant of RANK, called DENSE_RANK, which handles ties in a bit of a different way. You’ll also explore a more robust version of the functions you saw in the previous lesson using NTH_VALUE. Next, you’ll create “buckets” of data using NTILE, which is more useful than you may think. You’ll also pick up a nifty little tool called CUME_DIST to find the number of records less than or equal to a certain record in a window. You’ll wrap up the chapter with one of the most powerful applications of window functions you’ve seen so far; LAG and LEAD.

More Ranking Functions

Marketing the right vehicles

Optimizing charging scheduling

Setting a minimum charging cost

NTILE and CUME_DIST

Surveying vehicle owners

Evaluating charging duration

How long are vehicle owners going between charges?

LAG and LEAD

Looking into the past!

Maintaining charger efficiency

Lightning charger load

Ranking Window Functions

You’ll start this final chapter with aggregation functions like AVG, COUNT, and SUM. You’ll compare the output of these functions to individual records in a  window, as well as to perform additional calculations. After this, you’ll master the most exciting application of window functions; running and moving calculations! You’ll start by calculating running averages and totals for different metrics for electric vehicle charging. Finally, you’ll wrap up the course by generating moving totals and averages with a sliding window!

Aggregate window functions

Engaging vehicle owners with metrics

Visualizing charging session duration and cost

Who's consuming the least?

Window frames

Are charging durations getting longer?

Identifying inefficient chargers

Moving Averages and Totals

Moving vs. running

Impact of temperature on charging rate

Putting it all together!

Congratulations!

Aggregate Window Functions

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!

Data Manipulation in Snowflake

Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.

Window Functions in Snowflake

Ingénieur de données associé dans Snowflake

    user_id,
    vehicle_model,
    battery_capacity,

    ROW_NUMBER() OVER(ORDER BY user_id) AS charging_row_number

Building a window function

Exercice interactif pratique