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.

Assigning row numbers

Building a window function

Adding row numbers to charging sessions

Riding the heat wave

Ranking window functions

Was that a fast charge?

Comparing energy consumed

Partitioning data in a window function

Follow the money!

Digging into charging costs

Window Functions

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.

Fungsi Peringkat Lanjutan

Memasarkan kendaraan yang tepat

Mengoptimalkan penjadwalan pengisian daya

Menetapkan biaya pengisian minimum

NTILE dan CUME_DIST

Mensurvei pemilik kendaraan

Mengevaluasi durasi pengisian daya

Seberapa lama pemilik kendaraan jeda antar pengisian daya?

LAG dan LEAD

Menggunakan fungsi jendela `LAG`

Melihat ke masa lalu!

Menjaga efisiensi pengisi daya

Beban pengisi daya cepat

Ranking Window Functions

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!

Aggregate window functions

Engaging vehicle owners with metrics

Visualizing charging session duration and cost

Who's consuming the least?

Window frames

Are charging durations getting longer?

Identifying inefficient chargers

Moving Averages and Totals

Moving vs. running

Impact of temperature on charging rate

Putting it all together!

Congratulations!

Aggregate Window Functions

Buka beragam alat pemecahan masalah baru dengan kekuatan fungsi window di Snowflake! Dalam kursus ini, Anda akan menguasai alat yang diperlukan untuk menyelesaikan masalah seperti mengidentifikasi pencilan dalam himpunan data Anda dan menghitung rata-rata bergerak.

Pertama, Anda akan membedakan antara fungsi agregasi tradisional dan fungsi window. Anda akan memahami anatomi fungsi window dengan memberikan nomor baris dan peringkat pada semua rekaman dalam kueri Snowflake. Setelah dasar-dasarnya kuat, Anda akan memasangkan fungsi window ini dengan partisi. Ini memberi Anda kemampuan untuk membuat kelompok rekaman yang terurut, serta membandingkan nilai-nilai berurutan.


Anda akan menutup kursus dengan fungsi window agregat dan rata-rata bergulir; dua penerapan fungsi window yang paling berguna untuk merapikan dan menganalisis data. Pada akhirnya, Anda akan memiliki seperangkat keterampilan baru yang akan meningkatkan performa kueri Snowflake Anda!

Data Manipulation in Snowflake

Temukan fungsi jendela Snowflake untuk menyelesaikan masalah data dengan peringkat, partisi, dan kalkulasi bergulir.

Fungsi Window di Snowflake

Temukan fungsi jendela Snowflake untuk menyelesaikan masalah data kompleks dengan peringkat, partisi, dan perhitungan bergulir.

Associate Data Engineer di Snowflake

SELECT
    user_id,
    charging_station_id,
    charging_start_time,
    charging_duration,

	charging_duration - LAG(charging_rate, 1, 0) OVER(

		PARTITION BY charging_station_id
		ORDER BY charging_start_time

	) AS change_in_charging_duration  -- Alias for column using LAG

Melihat ke masa lalu!

Fungsi Window di Snowflake

Latihan interaktif praktis