Congratulations!

1. Congratulations!

Congratulations! This wasn't an easy course, but you've made it all the way through!

2. Window functions

You started by comparing window functions to aggregation functions to understand how window functions allow calculations and operations to be performed on "windows" or records without losing row-level detail.

3. Ranking window functions

The first window functions that you applied were "ranking" window functions like `RANK`, `DENSE_RANK`, and `NTH_VALUE`. This allowed for you to do things like assign an order to workouts based on their length or calories burned.

4. Aggregation window functions

Using aggregation functions, you were able to compute metrics like the `COUNT`, `AVG`, or `SUM` of a field in a window and compare individual records to that calculation. This was especially useful for generating gym member summaries and reports, like this one here.

5. Running and moving calculations

Finally, you mastered one of the most challenging parts of window functions; dynamic window frames. You calculated running and moving calculations, like the moving average of calories burned showed here.

6. What's next?

Now, it's time to keep growing your Snowflake acumen. Check out a few of these DataCamp resources for your next learning journey!

7. Thank you!

Thanks for joining me, and best of luck as you continue along in the data world!

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This exercise is part of the course

Window Functions in Snowflake

IntermediateSkill Level

4.9+

Start Course for Free

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.

Exercise 1: Assigning row numbers Exercise 2: Building a window function Exercise 3: Adding row numbers to charging sessions Exercise 4: Riding the heat wave Exercise 5: Ranking window functions Exercise 6: Was that a fast charge?Exercise 7: Comparing energy consumed Exercise 8: Partitioning data in a window function Exercise 9: Follow the money!Exercise 10: Digging into charging costs

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.

Exercise 1: More Ranking Functions Exercise 2: Marketing the right vehicles Exercise 3: Optimizing charging scheduling Exercise 4: Setting a minimum charging cost Exercise 5: NTILE and CUME_DIST Exercise 6: Surveying vehicle owners Exercise 7: Evaluating charging duration Exercise 8: How long are vehicle owners going between charges?Exercise 9: LAG and LEAD Exercise 10: Looking into the past!Exercise 11: Maintaining charger efficiency Exercise 12: Lightning charger load

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!

Exercise 1: Aggregate window functions Exercise 2: Engaging vehicle owners with metrics Exercise 3: Visualizing charging session duration and cost Exercise 4: Who's consuming the least?Exercise 5: Window frames Exercise 6: Are charging durations getting longer?Exercise 7: Identifying inefficient chargers Exercise 8: Moving Averages and Totals Exercise 9: Moving vs. running Exercise 10: Impact of temperature on charging rate Exercise 11: Putting it all together!Exercise 12: Congratulations!

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