Instruction Write a query to return DoD(day over day) growth for each store from May 25 (inclusive) to May 31 (inclusive). DoD: (current_day/ prev_day -1) * 100.0 Multiply dod growth to 100.0 to get percentage of growth. Use ROUND to convert dod growth to the nearest inte…

Prev Discuss

Question 80

DoD revenue growth for each store

Unsolved Hard Lecture

Premium Access

Unlock this premium interview question.

230+ interview questions
Official solutions
SQL, Python, and R tracks

***********

***** * ***** ** ****** ***(*** **** ***) ****** *** **** ***** **** *** 25 (*********) ** *** 31 (*********).
***: (*******_***/ ****_*** -1) * 100.0
******** *** ****** ** 100.0 ** *** ********** ** ******.
*** ***** ** ******* *** ****** ** *** ******* *******.
*** **********, **'* **** ** ****** ***** **** ******* ** **** *** *** **** *****

****

** **** *** **** ****, *** *** ********* *** ** ****** * *****'* ***** *******:

**** *****_*****_*** ** (
  ****** 
    *.*****_**, 
    ****(*.*******_**) ****,
    ***(******) ** *****_***
  **** 
    ******* *
  ***** **** ****** *
  ** *.******_** = *.******_**
  ***** **** ********* *
  ** *.*********_** = *.*********_**
  ***** ****(*.*******_**) >= '2020-05-01'
  *** ****(*.*******_**) <= '2020-05-31'
  ***** ** *.*****_**, ****(*.*******_**)
)

Table 1: inventory

**** *** ** ******, ********_** ** *** ******* *** ** **** *****.

   ***_****   | ***_****
--------------+--------------------------
 *********_** | *******
 ****_**      | ********
 *****_**     | ********

Table 2: payment

***** ****** ******* ************ *****

   ***_****   | ***_****
--------------+--------------------------
 *******_**   | *******
 ********_**  | ********
 *****_**     | ********
 ******_**    | *******
 ******       | *******
 *******_**   | ********* **** **** ****

Table 3: rental

   ***_****   | ***_****
--------------+--------------------------
 ******_**    | *******
 ******_**    | ********* **** **** ****
 *********_** | *******
 ********_**  | ********
 ******_**    | ********* **** **** ****
 *****_**     | ********

Sample results

Last Submission

postgres

No submission yet for this engine. Run and submit your query to save it here.

Expected results

Submit a query to compare against expected output.

Engine

Interview timer

Recommended interview pacing

Easy: 5 min for direct warm-up style questions.

Medium: 10 min for multi-step interview queries.

Hard: 15 min for layered questions with tighter time pressure.

A common bar is solving about 2 medium-or-harder questions in a 30 minute interview.

15:00

Timer

Your results

Run your query to preview results here.

More LAG, LEAD Questions

Browse Topic Set

Question	Level	Company	FTPR
#230. Customer Purchase Trend Analysis	Easy	microsoft
#229. Time Between Repeat Purchases	Hard	microsoft
#199. Students improvement	Hard	snap	67%
#195. MoM user growth	Hard	snap	14%
#180. Members moved from Microsoft to Google directly.	Hard	linkedin	21%
#147. 2 days streak customers	Hard	google	13%
#121. Week over week change of first ever bookings	Hard	airbnb	14%
#114. Dod revenue change rate	Easy	amazon	15%
#79. Spend difference between the last and the second last rentals	Hard		16%
#69. Number of happy customers	Hard		7%
#68. Spend difference between first and second rentals	Hard		12%