Hoe lang wachten voertuigbezitters tussen laadbeurten?

Het is handig om te begrijpen hoe lang voertuigbezitters tussen laadsessies wachten. De kolom distance_driven is hier een goede benadering voor: grotere afstanden betekenen meestal langere tijden tussen laadbeurten. Dit helpt het finance-team om omzet te voorspellen en het operations-team om laders goed toe te wijzen. Jij gaat voertuigbezitters in groepen indelen om deze patronen beter te begrijpen.

Je werkt met het schema ELECTRIC_VEHICLES in de database die begint met COURSE_41767.

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Vensterfuncties in Snowflake

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Vensterfuncties in Snowflake

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Begin de cursus gratis

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: Meer rankingfuncties Exercise 2: De juiste voertuigen marketen Exercise 3: Laadplanning optimaliseren Exercise 4: Een minimale laadkost instellen Exercise 5: NTILE en CUME_DIST Exercise 6: Voertuigeigenaren ondervragen Exercise 7: Laadduur evalueren Exercise 8: Hoe lang wachten voertuigbezitters tussen laadbeurten?

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Exercise 9: LAG en LEAD Exercise 10: Een blik in het verleden!Exercise 11: Laadpaalefficiëntie behouden Exercise 12: Belasting van snelladers

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!