Maintaining charger efficiency

As vehicle chargers age, their efficiency often wanes. The operations team is interested in performing proactive maintenance and identifying when a charger may need to be replaced. To do this, they'd like to have a report that tracks the change in charging rate in the last three charges. Let's see if you can give them a hand!

This exercise is part of the course

Window Functions in Snowflake

Exercise instructions

Use a window function to find the difference between the charging_rate for the current session and the charging_rate from 3 sessions ago.
Partition the result set data by the charging_station_location to make sure that results are isolated to each charging station.
Determine the sequence of sessions using the charging_start_time, in ascending order.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

SELECT
    user_id,
    charging_station_location,
    charging_start_time,
    charging_rate,
	
    -- Difference between current charging rate versus three sessions ago
    ___ - ___(___, ___, 0) OVER(
      	
      	-- Make sure results are partitioned by charging_station_location
        ___
      
      	-- Sessions should be ordered by when charging began
        ___
      
    ) AS change_in_charging_rate

FROM ELECTRIC_VEHICLES.charging;

Edit and Run Code

This exercise is part of the course

Window Functions in Snowflake

IntermediateSkill Level

4.9+

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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

Current Exercise

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!