Surf mee op de hittegolf

Nu ga je de Snowflake-interface gebruiken om de database te verkennen en bevragen die begint met COURSE_41767 en waar je in deze cursus mee werkt.

Je hebt queries geschreven in de DataCamp-editor, en nu pas je je ROW_NUMBER-skills direct toe in de interface van Snowflake.

Opmerking: onze Snowflake draait in een virtuele Windows-omgeving en kan in het begin enkele seconden nodig hebben om te laden.

Deze oefening maakt deel uit van de cursus

Vensterfuncties in Snowflake

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Vensterfuncties in Snowflake

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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: Volgnummers toekennen Exercise 2: Een windowfunctie opbouwen Exercise 3: Rijnummers toevoegen aan laadsessies Exercise 4: Surf mee op de hittegolf

Huidige oefening

Exercise 5: Rangschikkende windowfuncties Exercise 6: Was dat een snelle laadbeurt?Exercise 7: Verbruikte energie vergelijken Exercise 8: Data partitioneren in een windowfunctie Exercise 9: Volg het geld!Exercise 10: Dieper in de laadkosten duiken

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!