Araç sahipleri şarjlar arasında ne kadar süre geçiyor?

Araç sahiplerinin şarj seansları arasında ne kadar süre geçtiğini anlamak faydalıdır. distance_driven sütunu bunun için iyi bir vekil değişkendir — daha büyük mesafeler genellikle şarjlar arasında daha uzun zamanlar anlamına gelir. Bu, finans ekibinin geliri öngörmesine ve operasyon ekibinin şarj cihazlarını doğru şekilde tahsis etmesine yardımcı olur. Görevin, bu kalıpları daha iyi anlamak için araç sahiplerini gruplara ayırmak.

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Kursa Ücretsiz Başlayın

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: Daha Fazla Sıralama Fonksiyonu Exercise 2: Doğru araçları pazarlamak Exercise 3: Şarj planlamasını optimize etme Exercise 4: Asgari şarj maliyetini belirleme Exercise 5: NTILE ve CUME_DIST Exercise 6: Araç sahipleriyle anket Exercise 7: Şarj süresini değerlendirme Exercise 8: Araç sahipleri şarjlar arasında ne kadar süre geçiyor?

Geçerli Egzersiz

Exercise 9: LAG ve LEAD Exercise 10: Geçmişe bakış!Exercise 11: Şarj cihazı verimliliğini koruma Exercise 12: Hızlı şarj yükü

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!