Memasarkan kendaraan yang tepat

Tim pemasaran Anda ingin mengidentifikasi kendaraan dengan kapasitas baterai terbesar. Hal ini akan memungkinkan mereka bekerja sama dengan produsen mobil untuk memasarkan kendaraan tersebut di berbagai stasiun pengisian. Tugas Anda adalah membuat peringkat untuk kendaraan-kendaraan ini berdasarkan kapasitas baterai masing-masing.

Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

Petunjuk latihan

Pada pernyataan SELECT, ambil field vehicle_model dan battery_capacity dari tabel ELECTRIC_VEHICLES.charging.
Gunakan fungsi window untuk mengembalikan peringkat tanpa celah.
Tentukan peringkat berdasarkan battery_capacity, dari terbesar ke terkecil.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

SELECT

	-- Return the vehicle model and battery capacity
    ___,
    ___,
    
    -- Use a window function to return a ranking without gaps
    ___() OVER(
      	-- Battery capacity from greatest to least
        ___
    ) AS battery_capacity_rank
    
FROM ELECTRIC_VEHICLES.charging;

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus 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: Fungsi Peringkat Lanjutan Exercise 2: Memasarkan kendaraan yang tepat

Latihan Saat Ini

Exercise 3: Mengoptimalkan penjadwalan pengisian daya Exercise 4: Menetapkan biaya pengisian minimum Exercise 5: NTILE dan CUME_DIST Exercise 6: Mensurvei pemilik kendaraan Exercise 7: Mengevaluasi durasi pengisian daya Exercise 8: Seberapa lama pemilik kendaraan jeda antar pengisian daya?Exercise 9: LAG dan LEAD Exercise 10: Melihat ke masa lalu!Exercise 11: Menjaga efisiensi pengisi daya Exercise 12: Beban pengisi daya cepat

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!