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.

More Ranking Functions

Marketing the right vehicles

Optimizing charging scheduling

Setting a minimum charging cost

NTILE and CUME_DIST

Surveying vehicle owners

Evaluating charging duration

How long are vehicle owners going between charges?

LAG and LEAD

Looking into the past!

Maintaining charger efficiency

Lightning charger load

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!

Fungsi jendela agregat

Melibatkan pemilik kendaraan dengan metrik

Memvisualisasikan durasi dan biaya sesi pengisian daya

Siapa yang mengonsumsi paling sedikit?

Window frame

Apakah durasi pengisian makin lama?

Mengidentifikasi pengisi daya yang tidak efisien

Rata-Rata dan Total Bergerak

Moving vs. running

Dampak suhu terhadap laju pengisian daya

Menggabungkan semuanya!

Selamat!

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

Siapa yang mengonsumsi paling sedikit?

Fungsi Window di Snowflake

Latihan interaktif praktis