Case Study #4 - Data Bank

Please note that the case study information provided below has been sourced from the following link: https://8weeksqlchallenge.com/case-study-4/

Table of Contents

Case Study Introduction Problem Statement Database Schema Question and Solution Database Creation & Table Populating Questions Sets A. Customer Nodes Exploration B. Customer Transactions C. Data Allocation Challenge SOLUTION Approach Breaking this down, CTE by CTE What I actually took away from this D. Extra Challenge SOLUTION Calculation approach SQL Query Breaking this down Monthly Data Requirement And what about compounding What I took away from this one Extension Request SOLUTION 2 Cents

Case Study Introduction

Danny does this thing. Sees a trend, doesnt stop at the obvious version of it. This time it was neo-banks. Digital only, no branches, everyone excited about it. Danny looks at it and thinks, why stop there. What if the bank also stored your data. What if crypto's whole "distributed nodes" thing found its way in too. So Data Bank happens.

Simple idea on the surface. Customers get cloud storage based on how much money they have in their account. More balance, more storage. I didnt think much of it at first, just seemed like a cute gimmick. Took me a few queries into the transactions table to realize this is actually a forecasting problem. Not a banking problem.

The team wants more customers. Fair enough, everyone wants growth. But nobody had worked out what more customers actually costs them in storage. Thats the real ask here. Not vanity metrics. Actual capacity planning.

So thats the job. Help them understand what they're sitting on before they go add a few thousand more customers on top of it.

Problem Statement

Three tables this time.

regions – five regions, thats it, just the map
customer_nodes – which node holds a customer's money and data, and for how long, since these get reshuffled constantly for security
customer_transactions – every deposit, withdrawal, purchase. This is where the real work is

Data Bank wants three things from us. First, basic exploration, how many nodes, how customers are spread across regions, nothing fancy. Second, real transaction behaviour, deposits, closing balances, who's actually growing their money month to month. Third, and this is the one that matters, modelling three different ways to allocate storage. End of month balance. 30 day rolling average. Real time. Management needs to pick one and they need numbers to pick it with, not a gut feeling.

There's a bonus too. Daily compounding interest. Because regular math apparently wasnt enough punishment.

One thing I keep reminding myself before writing a single query, what does one row actually mean here. A customer? A month? A node stay? Get that wrong early and every join after it lies to you politely. Learned this the hard way before. Not keen on relearning it.

Database Schema

(created using ChatGPT x Info on original site)

Question and Solution

I am going to use DuckDB SQL Workbench to solve these queries.

Database Creation & Table Populating

#1 Regions


CREATE SCHEMA IF NOT EXISTS data_bank;
DROP TABLE IF EXISTS regions;

--data_bank
SET search_path = data_bank;

create table data_bank.regions 
  ( region_id INTEGER,
  region_name VARCHAR(9)
  );

insert into data_bank.regions
  (region_id, region_name)
VALUES
  (1, 'Australia'),
  (2, 'America'),
  (3, 'Africa'),
  (4, 'Asia'),
  (5, 'Europe');

select * from data_bank.regions;

#2 customer_nodes


drop table if exists data_bank.customer_nodes;

create table data_bank.customer_nodes (
    customer_id integer,
    region_id integer,
    node_id integer,
    start_date date,
    end_date date
);

insert into data_bank.customer_nodes
  (customer_id, region_id, node_id, start_date, end_date)
VALUES
  (1, 3, 4, '2020-01-02', '2020-01-03'),
  (2, 3, 5, '2020-01-03', '2020-01-17'),
  (3, 5, 4, '2020-01-27', '2020-02-18'),
  (4, 5, 4, '2020-01-07', '2020-01-19'),
  (5, 3, 3, '2020-01-15', '2020-01-23'),
  (6, 1, 1, '2020-01-11', '2020-02-06'),
  (7, 2, 5, '2020-01-20', '2020-02-04'),
  (8, 1, 2, '2020-01-15', '2020-01-28'),
  (9, 4, 5, '2020-01-21', '2020-01-25'),
  (10, 3, 4, '2020-01-13', '2020-01-14'),
  (11, 2, 5, '2020-01-19', '2020-01-25'),
  (12, 1, 2, '2020-01-13', '2020-01-14'),
  (13, 2, 3, '2020-01-02', '2020-01-14'),
  (14, 1, 2, '2020-01-25', '2020-01-25'),
  (15, 1, 3, '2020-01-25', '2020-02-08'),
  (16, 4, 4, '2020-01-13', '2020-01-18'),
  (17, 2, 3, '2020-01-19', '2020-01-27'),
  (18, 1, 3, '2020-01-17', '2020-02-15'),
  (19, 2, 2, '2020-01-17', '2020-02-06'),
  (20, 2, 4, '2020-01-18', '2020-02-09'),
  (21, 3, 2, '2020-01-12', '2020-01-25'),
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  (23, 1, 5, '2020-01-21', '2020-02-15'),
  (24, 2, 5, '2020-01-26', '2020-02-14'),
  (25, 5, 1, '2020-01-28', '2020-02-10'),
  (26, 3, 3, '2020-01-17', '2020-01-25'),
  (27, 4, 3, '2020-01-01', '2020-01-22'),
  (28, 4, 2, '2020-01-20', '2020-02-13'),
  (29, 2, 3, '2020-01-19', '2020-01-31'),
  (30, 2, 1, '2020-01-26', '2020-02-06'),
  (31, 3, 2, '2020-01-06', '2020-01-13'),
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  (34, 2, 3, '2020-01-30', '2020-02-18'),
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  (38, 5, 1, '2020-01-21', '2020-02-14'),
  (39, 5, 2, '2020-01-22', '2020-01-23'),
  (40, 2, 1, '2020-01-21', '2020-02-17'),
  (41, 5, 1, '2020-01-30', '2020-02-26'),
  (42, 4, 3, '2020-01-11', '2020-01-16'),
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  (44, 3, 1, '2020-01-19', '2020-02-09'),
  (45, 2, 1, '2020-01-14', '2020-02-08'),
  (46, 1, 4, '2020-01-23', '2020-01-26'),
  (47, 5, 4, '2020-01-22', '2020-01-26'),
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  (50, 2, 5, '2020-01-29', '2020-02-06'),
  (51, 1, 3, '2020-01-20', '2020-02-02'),
  (52, 5, 2, '2020-01-12', '2020-01-26'),
  (53, 4, 1, '2020-01-24', '2020-01-29'),
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  (55, 5, 5, '2020-01-25', '2020-02-07'),
  (56, 4, 4, '2020-01-18', '2020-01-20'),
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  (58, 3, 2, '2020-01-11', '2020-01-31'),
  (59, 3, 1, '2020-01-20', '2020-02-03'),
  (60, 5, 5, '2020-01-19', '2020-01-20'),
  (61, 4, 5, '2020-01-21', '2020-01-26'),
  (62, 2, 1, '2020-01-16', '2020-02-11'),
  (63, 1, 3, '2020-01-06', '2020-02-01'),
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  (353, 3, 5, '2020-04-21', '9999-12-31'),
  (354, 5, 4, '2020-05-19', '9999-12-31'),
  (355, 3, 2, '2020-05-23', '9999-12-31'),
  (356, 5, 3, '2020-04-23', '9999-12-31'),
  (357, 4, 2, '2020-05-11', '9999-12-31'),
  (358, 3, 1, '2020-04-18', '9999-12-31'),
  (359, 2, 3, '2020-04-04', '9999-12-31'),
  (360, 5, 3, '2020-02-29', '9999-12-31'),
  (361, 5, 5, '2020-05-18', '9999-12-31'),
  (362, 5, 1, '2020-04-17', '9999-12-31'),
  (363, 5, 1, '2020-04-25', '9999-12-31'),
  (364, 4, 3, '2020-05-02', '9999-12-31'),
  (365, 2, 5, '2020-05-24', '9999-12-31'),
  (366, 2, 3, '2020-05-17', '9999-12-31'),
  (367, 3, 4, '2020-04-25', '9999-12-31'),
  (368, 1, 5, '2020-05-05', '9999-12-31'),
  (369, 4, 5, '2020-04-03', '9999-12-31'),
  (370, 5, 2, '2020-05-22', '9999-12-31'),
  (371, 5, 4, '2020-05-31', '9999-12-31'),
  (372, 2, 4, '2020-04-12', '9999-12-31'),
  (373, 5, 2, '2020-05-10', '9999-12-31'),
  (374, 2, 4, '2020-03-12', '9999-12-31'),
  (375, 1, 1, '2020-04-02', '9999-12-31'),
  (376, 5, 1, '2020-04-16', '9999-12-31'),
  (377, 4, 5, '2020-04-26', '9999-12-31'),
  (378, 2, 2, '2020-04-03', '9999-12-31'),
  (379, 5, 3, '2020-04-23', '9999-12-31'),
  (380, 1, 4, '2020-04-26', '9999-12-31'),
  (381, 1, 3, '2020-04-15', '9999-12-31'),
  (382, 4, 4, '2020-03-09', '9999-12-31'),
  (383, 2, 5, '2020-05-16', '9999-12-31'),
  (384, 3, 4, '2020-05-14', '9999-12-31'),
  (385, 2, 5, '2020-05-13', '9999-12-31'),
  (386, 1, 3, '2020-05-30', '9999-12-31'),
  (387, 1, 5, '2020-03-22', '9999-12-31'),
  (388, 3, 3, '2020-04-19', '9999-12-31'),
  (389, 4, 2, '2020-04-01', '9999-12-31'),
  (390, 2, 1, '2020-03-22', '9999-12-31'),
  (391, 3, 2, '2020-04-20', '9999-12-31'),
  (392, 2, 3, '2020-03-24', '9999-12-31'),
  (393, 2, 4, '2020-04-04', '9999-12-31'),
  (394, 4, 5, '2020-05-13', '9999-12-31'),
  (395, 1, 2, '2020-04-15', '9999-12-31'),
  (396, 3, 1, '2020-05-02', '9999-12-31'),
  (397, 4, 3, '2020-04-26', '9999-12-31'),
  (398, 3, 3, '2020-04-11', '9999-12-31'),
  (399, 3, 1, '2020-04-03', '9999-12-31'),
  (400, 4, 1, '2020-03-29', '9999-12-31'),
  (401, 1, 1, '2020-02-19', '9999-12-31'),
  (402, 2, 5, '2020-04-20', '9999-12-31'),
  (403, 3, 5, '2020-04-30', '9999-12-31'),
  (404, 5, 4, '2020-02-05', '9999-12-31'),
  (405, 4, 3, '2020-03-31', '9999-12-31'),
  (406, 5, 2, '2020-04-18', '9999-12-31'),
  (407, 3, 4, '2020-04-01', '9999-12-31'),
  (408, 5, 4, '2020-04-21', '9999-12-31'),
  (409, 5, 3, '2020-05-01', '9999-12-31'),
  (410, 2, 2, '2020-04-26', '9999-12-31'),
  (411, 2, 2, '2020-06-02', '9999-12-31'),
  (412, 4, 5, '2020-03-21', '9999-12-31'),
  (413, 3, 5, '2020-04-14', '9999-12-31'),
  (414, 1, 3, '2020-03-18', '9999-12-31'),
  (415, 5, 2, '2020-04-02', '9999-12-31'),
  (416, 1, 5, '2020-04-27', '9999-12-31'),
  (417, 1, 2, '2020-02-29', '9999-12-31'),
  (418, 1, 1, '2020-03-23', '9999-12-31'),
  (419, 1, 4, '2020-03-15', '9999-12-31'),
  (420, 2, 1, '2020-05-01', '9999-12-31'),
  (421, 3, 3, '2020-04-15', '9999-12-31'),
  (422, 1, 4, '2020-05-04', '9999-12-31'),
  (423, 4, 4, '2020-04-10', '9999-12-31'),
  (424, 1, 5, '2020-04-18', '9999-12-31'),
  (425, 3, 3, '2020-04-11', '9999-12-31'),
  (426, 1, 3, '2020-05-12', '9999-12-31'),
  (427, 4, 3, '2020-05-26', '9999-12-31'),
  (428, 2, 1, '2020-05-09', '9999-12-31'),
  (429, 5, 3, '2020-04-18', '9999-12-31'),
  (430, 2, 5, '2020-04-01', '9999-12-31'),
  (431, 3, 5, '2020-04-09', '9999-12-31'),
  (432, 1, 3, '2020-03-15', '9999-12-31'),
  (433, 4, 1, '2020-04-03', '9999-12-31'),
  (434, 5, 4, '2020-04-04', '9999-12-31'),
  (435, 3, 2, '2020-04-01', '9999-12-31'),
  (436, 3, 2, '2020-04-07', '9999-12-31'),
  (437, 1, 3, '2020-04-06', '9999-12-31'),
  (438, 4, 2, '2020-04-25', '9999-12-31'),
  (439, 3, 2, '2020-04-27', '9999-12-31'),
  (440, 5, 4, '2020-03-18', '9999-12-31'),
  (441, 4, 3, '2020-04-13', '9999-12-31'),
  (442, 1, 5, '2020-05-09', '9999-12-31'),
  (443, 2, 4, '2020-04-15', '9999-12-31'),
  (444, 3, 4, '2020-03-19', '9999-12-31'),
  (445, 2, 3, '2020-04-30', '9999-12-31'),
  (446, 3, 3, '2020-05-14', '9999-12-31'),
  (447, 5, 1, '2020-04-02', '9999-12-31'),
  (448, 3, 5, '2020-05-20', '9999-12-31'),
  (449, 3, 5, '2020-04-11', '9999-12-31'),
  (450, 3, 5, '2020-05-05', '9999-12-31'),
  (451, 1, 3, '2020-04-26', '9999-12-31'),
  (452, 2, 1, '2020-03-19', '9999-12-31'),
  (453, 1, 5, '2020-04-12', '9999-12-31'),
  (454, 2, 3, '2020-05-22', '9999-12-31'),
  (455, 1, 2, '2020-03-17', '9999-12-31'),
  (456, 2, 3, '2020-04-03', '9999-12-31'),
  (457, 1, 3, '2020-05-02', '9999-12-31'),
  (458, 4, 1, '2020-03-15', '9999-12-31'),
  (459, 3, 2, '2020-04-19', '9999-12-31'),
  (460, 3, 4, '2020-03-25', '9999-12-31'),
  (461, 1, 2, '2020-04-02', '9999-12-31'),
  (462, 1, 2, '2020-04-22', '9999-12-31'),
  (463, 4, 2, '2020-05-14', '9999-12-31'),
  (464, 1, 3, '2020-04-21', '9999-12-31'),
  (465, 4, 2, '2020-03-27', '9999-12-31'),
  (466, 1, 4, '2020-03-29', '9999-12-31'),
  (467, 3, 2, '2020-03-28', '9999-12-31'),
  (468, 4, 1, '2020-04-09', '9999-12-31'),
  (469, 2, 4, '2020-04-24', '9999-12-31'),
  (470, 3, 5, '2020-04-12', '9999-12-31'),
  (471, 2, 1, '2020-04-17', '9999-12-31'),
  (472, 4, 4, '2020-05-02', '9999-12-31'),
  (473, 5, 4, '2020-04-26', '9999-12-31'),
  (474, 3, 3, '2020-04-17', '9999-12-31'),
  (475, 2, 5, '2020-03-22', '9999-12-31'),
  (476, 3, 2, '2020-04-23', '9999-12-31'),
  (477, 3, 2, '2020-04-19', '9999-12-31'),
  (478, 3, 5, '2020-03-31', '9999-12-31'),
  (479, 3, 5, '2020-04-08', '9999-12-31'),
  (480, 2, 2, '2020-04-24', '9999-12-31'),
  (481, 1, 4, '2020-04-23', '9999-12-31'),
  (482, 3, 1, '2020-04-26', '9999-12-31'),
  (483, 5, 2, '2020-04-10', '9999-12-31'),
  (484, 3, 1, '2020-05-07', '9999-12-31'),
  (485, 2, 2, '2020-04-17', '9999-12-31'),
  (486, 3, 1, '2020-03-31', '9999-12-31'),
  (487, 3, 5, '2020-04-01', '9999-12-31'),
  (488, 5, 3, '2020-04-13', '9999-12-31'),
  (489, 4, 5, '2020-03-25', '9999-12-31'),
  (490, 5, 3, '2020-03-28', '9999-12-31'),
  (491, 3, 5, '2020-05-13', '9999-12-31'),
  (492, 1, 4, '2020-05-06', '9999-12-31'),
  (493, 4, 3, '2020-03-19', '9999-12-31'),
  (494, 5, 5, '2020-04-14', '9999-12-31'),
  (495, 5, 4, '2020-05-07', '9999-12-31'),
  (496, 3, 4, '2020-02-25', '9999-12-31'),
  (497, 5, 4, '2020-05-27', '9999-12-31'),
  (498, 1, 2, '2020-04-05', '9999-12-31'),
  (499, 5, 1, '2020-02-03', '9999-12-31'),
  (500, 2, 2, '2020-04-15', '9999-12-31');

select *
from data_bank.customer_nodes
order by customer_id, start_date;

#3 customer_transactions


--customer_transaction
create schema if not exists data_bank;

drop table if exists data_bank.customer_transactions;

create table data_bank.customer_transactions (
    customer_id integer,
    txn_date date,
    txn_type varchar(10),
    txn_amount integer
);
insert into data_bank.customer_transactions
  (customer_id, txn_date, txn_type, txn_amount)
VALUES
  (429, '2020-01-21', 'deposit', 82),
  (155, '2020-01-10', 'deposit', 712),
  (398, '2020-01-01', 'deposit', 196),
  (255, '2020-01-14', 'deposit', 563),
  (185, '2020-01-29', 'deposit', 626),
  (309, '2020-01-13', 'deposit', 995),
  (312, '2020-01-20', 'deposit', 485),
  (376, '2020-01-03', 'deposit', 706),
  (188, '2020-01-13', 'deposit', 601),
  (138, '2020-01-11', 'deposit', 520),
  (373, '2020-01-18', 'deposit', 596),
  (361, '2020-01-12', 'deposit', 797),
  (169, '2020-01-10', 'deposit', 628),
  (402, '2020-01-05', 'deposit', 435),
  (60, '2020-01-19', 'deposit', 495),
  (378, '2020-01-07', 'deposit', 193),
  (383, '2020-01-26', 'deposit', 889),
  (292, '2020-01-10', 'deposit', 136),
  (63, '2020-01-06', 'deposit', 234),
  (499, '2020-01-02', 'deposit', 147),
  (130, '2020-01-02', 'deposit', 557),
  (441, '2020-01-12', 'deposit', 418),
  (53, '2020-01-24', 'deposit', 22),
  (30, '2020-01-26', 'deposit', 33),
  (305, '2020-01-09', 'deposit', 36),
  (136, '2020-01-11', 'deposit', 882),
  (276, '2020-01-07', 'deposit', 926),
  (410, '2020-01-07', 'deposit', 601),
  (152, '2020-01-01', 'deposit', 917),
  (123, '2020-01-16', 'deposit', 423),
  (17, '2020-01-19', 'deposit', 465),
  (472, '2020-01-18', 'deposit', 495),
  (100, '2020-01-06', 'deposit', 158),
  (56, '2020-01-18', 'deposit', 864),
  (455, '2020-01-07', 'deposit', 329),
  (357, '2020-01-20', 'deposit', 780),
  (480, '2020-01-29', 'deposit', 522),
  (168, '2020-01-13', 'deposit', 114),
  (369, '2020-01-25', 'deposit', 376),
  (338, '2020-01-17', 'deposit', 628),
  (296, '2020-01-13', 'deposit', 846),
  (374, '2020-01-08', 'deposit', 117),
  (194, '2020-01-28', 'deposit', 137),
  (135, '2020-01-09', 'deposit', 949),
  (21, '2020-01-12', 'deposit', 28),
  (69, '2020-01-10', 'deposit', 124),
  (32, '2020-01-12', 'deposit', 812),
  (206, '2020-01-09', 'deposit', 811),
  (269, '2020-01-14', 'deposit', 654),
  (460, '2020-01-29', 'deposit', 80),
  (328, '2020-01-22', 'deposit', 393),
  (407, '2020-01-14', 'deposit', 804),
  (351, '2020-01-03', 'deposit', 951),
  (458, '2020-01-04', 'deposit', 715),
  (339, '2020-01-10', 'deposit', 167),
  (496, '2020-01-06', 'deposit', 47),
  (291, '2020-01-20', 'deposit', 930),
  (50, '2020-01-29', 'deposit', 899),
  (265, '2020-01-08', 'deposit', 699),
  (266, '2020-01-17', 'deposit', 651),
  (93, '2020-01-11', 'deposit', 557),
  (1, '2020-01-02', 'deposit', 312),
  (267, '2020-01-19', 'deposit', 31),
  (366, '2020-01-21', 'deposit', 965),
  (92, '2020-01-05', 'deposit', 985),
  (89, '2020-01-25', 'deposit', 210),
  (129, '2020-01-12', 'deposit', 568),
  (198, '2020-01-17', 'deposit', 571),
  (26, '2020-01-17', 'deposit', 878),
  (104, '2020-01-25', 'deposit', 989),
  (466, '2020-01-17', 'deposit', 80),
  (454, '2020-01-08', 'deposit', 603),
  (117, '2020-01-15', 'deposit', 5),
  (46, '2020-01-23', 'deposit', 356),
  (96, '2020-01-03', 'deposit', 753),
  (199, '2020-01-20', 'deposit', 530),
  (55, '2020-01-25', 'deposit', 380),
  (443, '2020-01-28', 'deposit', 760),
  (317, '2020-01-11', 'deposit', 869),
  (52, '2020-01-12', 'deposit', 908),
  (249, '2020-01-14', 'deposit', 336),
  (122, '2020-01-29', 'deposit', 397),
  (59, '2020-01-20', 'deposit', 924),
  (36, '2020-01-30', 'deposit', 149),
  (74, '2020-01-13', 'deposit', 229),
  (238, '2020-01-18', 'deposit', 896),
  (334, '2020-01-15', 'deposit', 933),
  (143, '2020-01-29', 'deposit', 807),
  (406, '2020-01-26', 'deposit', 795),
  (465, '2020-01-28', 'deposit', 955),
  (230, '2020-01-21', 'deposit', 675),
  (487, '2020-01-15', 'deposit', 39),
  (346, '2020-01-21', 'deposit', 916),
  (356, '2020-01-11', 'deposit', 568),
  (142, '2020-01-16', 'deposit', 517),
  (81, '2020-01-25', 'deposit', 403),
  (128, '2020-01-26', 'deposit', 410),
  (205, '2020-01-02', 'deposit', 608),
  (380, '2020-01-03', 'deposit', 487),
  (387, '2020-01-22', 'deposit', 180),
  (95, '2020-01-03', 'deposit', 19),
  (388, '2020-01-09', 'deposit', 833),
  (91, '2020-01-11', 'deposit', 856),
  (144, '2020-01-14', 'deposit', 559),
  (68, '2020-01-20', 'deposit', 574),
  (475, '2020-01-03', 'deposit', 552),
  (247, '2020-01-01', 'deposit', 930),
  (120, '2020-01-23', 'deposit', 824),
  (500, '2020-01-16', 'deposit', 227),
  (384, '2020-01-12', 'deposit', 352),
  (311, '2020-01-17', 'deposit', 918),
  (287, '2020-01-22', 'deposit', 658),
  (112, '2020-01-08', 'deposit', 945),
  (284, '2020-01-30', 'deposit', 257),
  (67, '2020-01-22', 'deposit', 79),
  (70, '2020-01-08', 'deposit', 786),
  (236, '2020-01-27', 'deposit', 356),
  (234, '2020-01-03', 'deposit', 236),
  (420, '2020-01-24', 'deposit', 151),
  (430, '2020-01-20', 'deposit', 829),
  (330, '2020-01-26', 'deposit', 540),
  (162, '2020-01-10', 'deposit', 123),
  (394, '2020-01-04', 'deposit', 854),
  (217, '2020-01-08', 'deposit', 783),
  (5, '2020-01-15', 'deposit', 974),
  (382, '2020-01-03', 'deposit', 140),
  (137, '2020-01-06', 'deposit', 881),
  (327, '2020-01-14', 'deposit', 299),
  (412, '2020-01-01', 'deposit', 381),
  (257, '2020-01-20', 'deposit', 493),
  (416, '2020-01-16', 'deposit', 756),
  (320, '2020-01-10', 'deposit', 725),
  (109, '2020-01-01', 'deposit', 429),
  (348, '2020-01-22', 'deposit', 26),
  (375, '2020-01-19', 'deposit', 647),
  (344, '2020-01-07', 'deposit', 816),
  (261, '2020-01-25', 'deposit', 746),
  (166, '2020-01-22', 'deposit', 957),
  (367, '2020-01-29', 'deposit', 239),
  (352, '2020-01-21', 'deposit', 416),
  (108, '2020-01-30', 'deposit', 530),
  (481, '2020-01-02', 'deposit', 42),
  (331, '2020-01-17', 'deposit', 951),
  (133, '2020-01-10', 'deposit', 279),
  (293, '2020-01-15', 'deposit', 541),
  (172, '2020-01-12', 'deposit', 548),
  (215, '2020-01-27', 'deposit', 822),
  (218, '2020-01-29', 'deposit', 208),
  (20, '2020-01-18', 'deposit', 868),
  (365, '2020-01-25', 'deposit', 595),
  (98, '2020-01-15', 'deposit', 622),
  (354, '2020-01-23', 'deposit', 822),
  (10, '2020-01-13', 'deposit', 556),
  (61, '2020-01-21', 'deposit', 319),
  (160, '2020-01-17', 'deposit', 843),
  (161, '2020-01-08', 'deposit', 526),
  (147, '2020-01-15', 'deposit', 767),
  (301, '2020-01-20', 'deposit', 32),
  (14, '2020-01-25', 'deposit', 205),
  (288, '2020-01-13', 'deposit', 586),
  (219, '2020-01-06', 'deposit', 419),
  (308, '2020-01-14', 'deposit', 782),
  (418, '2020-01-07', 'deposit', 688),
  (167, '2020-01-30', 'deposit', 51),
  (350, '2020-01-13', 'deposit', 167),
  (225, '2020-01-10', 'deposit', 280),
  (187, '2020-01-26', 'deposit', 599),
  (396, '2020-01-01', 'deposit', 334),
  (304, '2020-01-16', 'deposit', 848),
  (343, '2020-01-01', 'deposit', 859),
  (62, '2020-01-16', 'deposit', 218),
  (364, '2020-01-10', 'deposit', 563),
  (207, '2020-01-26', 'deposit', 322),
  (223, '2020-01-11', 'deposit', 431),
  (73, '2020-01-06', 'deposit', 442),
  (299, '2020-01-13', 'deposit', 881),
  (16, '2020-01-13', 'deposit', 421),
  (212, '2020-01-02', 'deposit', 336),
  (381, '2020-01-28', 'deposit', 66),
  (390, '2020-01-15', 'deposit', 102),
  (165, '2020-01-23', 'deposit', 42),
  (442, '2020-01-26', 'deposit', 553),
  (332, '2020-01-27', 'deposit', 594),
  (264, '2020-01-16', 'deposit', 41),
  (64, '2020-01-08', 'deposit', 442),
  (428, '2020-01-15', 'deposit', 280),
  (227, '2020-01-07', 'deposit', 468),
  (116, '2020-01-28', 'deposit', 167),
  (134, '2020-01-08', 'deposit', 358),
  (192, '2020-01-20', 'deposit', 906),
  (452, '2020-01-01', 'deposit', 441),
  (436, '2020-01-05', 'deposit', 401),
  (176, '2020-01-26', 'deposit', 655),
  (408, '2020-01-21', 'deposit', 514),
  (490, '2020-01-23', 'deposit', 271),
  (111, '2020-01-28', 'deposit', 101),
  (372, '2020-01-02', 'deposit', 920),
  (307, '2020-01-14', 'deposit', 363),
  (363, '2020-01-06', 'deposit', 622),
  (22, '2020-01-19', 'deposit', 794),
  (171, '2020-01-17', 'deposit', 471),
  (19, '2020-01-17', 'deposit', 47),
  (289, '2020-01-28', 'deposit', 838),
  (419, '2020-01-11', 'deposit', 711),
  (154, '2020-01-15', 'deposit', 120),
  (156, '2020-01-24', 'deposit', 82),
  (75, '2020-01-22', 'deposit', 234),
  (191, '2020-01-16', 'deposit', 769),
  (71, '2020-01-14', 'deposit', 128),
  (263, '2020-01-16', 'deposit', 312),
  (462, '2020-01-26', 'deposit', 614),
  (405, '2020-01-04', 'deposit', 413),
  (37, '2020-01-29', 'deposit', 946),
  (342, '2020-01-25', 'deposit', 347),
  (371, '2020-01-21', 'deposit', 528),
  (173, '2020-01-14', 'deposit', 720),
  (9, '2020-01-21', 'deposit', 669),
  (302, '2020-01-27', 'deposit', 143),
  (403, '2020-01-13', 'deposit', 232),
  (110, '2020-01-01', 'deposit', 888),
  (492, '2020-01-05', 'deposit', 812),
  (477, '2020-01-04', 'deposit', 101),
  (337, '2020-01-12', 'deposit', 581),
  (345, '2020-01-01', 'deposit', 555),
  (131, '2020-01-10', 'deposit', 932),
  (58, '2020-01-11', 'deposit', 726),
  (99, '2020-01-08', 'deposit', 160),
  (488, '2020-01-08', 'deposit', 304),
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  (41, '2020-02-06', 'deposit', 84),
  (41, '2020-03-22', 'deposit', 232),
  (41, '2020-03-25', 'deposit', 872),
  (41, '2020-03-26', 'deposit', 65),
  (41, '2020-03-15', 'purchase', 397),
  (41, '2020-03-12', 'purchase', 293),
  (41, '2020-01-31', 'withdrawal', 836),
  (41, '2020-03-05', 'deposit', 914),
  (41, '2020-02-23', 'deposit', 898),
  (41, '2020-03-30', 'purchase', 136),
  (41, '2020-04-04', 'withdrawal', 177),
  (39, '2020-04-09', 'withdrawal', 529),
  (39, '2020-02-19', 'withdrawal', 281),
  (39, '2020-04-17', 'deposit', 933),
  (39, '2020-03-27', 'withdrawal', 55),
  (39, '2020-02-15', 'deposit', 792),
  (39, '2020-01-22', 'deposit', 433),
  (39, '2020-03-25', 'withdrawal', 409),
  (39, '2020-04-09', 'deposit', 939),
  (39, '2020-04-13', 'withdrawal', 644),
  (39, '2020-02-27', 'purchase', 233),
  (39, '2020-02-10', 'deposit', 73),
  (39, '2020-02-01', 'deposit', 608),
  (39, '2020-04-08', 'withdrawal', 240),
  (39, '2020-03-04', 'deposit', 536),
  (39, '2020-04-04', 'purchase', 28),
  (39, '2020-04-01', 'purchase', 375),
  (235, '2020-03-16', 'deposit', 530),
  (235, '2020-02-06', 'deposit', 428),
  (235, '2020-02-22', 'deposit', 535),
  (235, '2020-01-22', 'purchase', 890),
  (235, '2020-02-25', 'deposit', 524),
  (235, '2020-03-20', 'withdrawal', 30),
  (235, '2020-01-31', 'purchase', 877),
  (235, '2020-01-10', 'purchase', 919),
  (203, '2020-01-09', 'purchase', 650),
  (203, '2020-01-25', 'deposit', 293),
  (203, '2020-03-07', 'purchase', 647),
  (203, '2020-02-26', 'purchase', 416),
  (203, '2020-03-27', 'deposit', 169),
  (203, '2020-03-31', 'purchase', 608),
  (203, '2020-02-29', 'deposit', 639),
  (203, '2020-03-29', 'deposit', 520),
  (203, '2020-02-17', 'deposit', 670),
  (203, '2020-01-20', 'deposit', 789),
  (203, '2020-01-18', 'deposit', 812),
  (203, '2020-02-27', 'purchase', 133),
  (203, '2020-03-16', 'deposit', 404),
  (203, '2020-03-14', 'deposit', 510),
  (203, '2020-01-29', 'purchase', 4),
  (203, '2020-04-04', 'withdrawal', 24),
  (203, '2020-02-21', 'withdrawal', 622),
  (203, '2020-01-08', 'deposit', 318),
  (203, '2020-03-10', 'purchase', 358),
  (203, '2020-02-05', 'withdrawal', 57),
  (203, '2020-02-07', 'deposit', 862),
  (119, '2020-04-10', 'deposit', 417),
  (119, '2020-03-09', 'withdrawal', 969),
  (233, '2020-02-08', 'deposit', 234),
  (233, '2020-02-28', 'purchase', 528),
  (233, '2020-02-14', 'deposit', 572),
  (233, '2020-03-01', 'deposit', 832),
  (233, '2020-01-08', 'deposit', 833),
  (233, '2020-02-12', 'deposit', 837),
  (233, '2020-01-21', 'purchase', 471),
  (233, '2020-01-23', 'deposit', 445),
  (233, '2020-01-17', 'deposit', 801),
  (451, '2020-04-06', 'withdrawal', 117),
  (451, '2020-04-09', 'withdrawal', 595),
  (451, '2020-02-28', 'withdrawal', 807),
  (451, '2020-02-06', 'purchase', 853),
  (451, '2020-03-13', 'deposit', 668),
  (451, '2020-04-19', 'deposit', 601),
  (451, '2020-02-19', 'withdrawal', 563),
  (392, '2020-02-07', 'deposit', 223),
  (392, '2020-03-04', 'purchase', 120),
  (392, '2020-04-01', 'withdrawal', 503),
  (392, '2020-04-12', 'purchase', 70),
  (392, '2020-03-09', 'withdrawal', 416),
  (392, '2020-02-06', 'deposit', 995),
  (392, '2020-01-29', 'withdrawal', 60),
  (392, '2020-04-21', 'deposit', 328),
  (491, '2020-02-24', 'deposit', 512),
  (491, '2020-01-19', 'withdrawal', 837),
  (491, '2020-03-12', 'deposit', 892),
  (491, '2020-02-07', 'deposit', 3),
  (491, '2020-02-09', 'deposit', 442),
  (491, '2020-03-04', 'withdrawal', 921),
  (491, '2020-03-08', 'purchase', 526),
  (491, '2020-03-18', 'withdrawal', 759),
  (491, '2020-03-04', 'withdrawal', 334),
  (491, '2020-01-27', 'deposit', 943),
  (491, '2020-03-28', 'purchase', 969),
  (491, '2020-01-30', 'purchase', 127),
  (491, '2020-02-11', 'withdrawal', 656),
  (393, '2020-03-05', 'deposit', 867),
  (393, '2020-04-04', 'withdrawal', 68),
  (393, '2020-03-29', 'withdrawal', 356),
  (393, '2020-03-29', 'deposit', 776),
  (393, '2020-02-04', 'withdrawal', 62),
  (393, '2020-02-08', 'deposit', 98),
  (393, '2020-02-01', 'purchase', 577),
  (393, '2020-03-17', 'withdrawal', 727),
  (393, '2020-03-25', 'purchase', 327),
  (393, '2020-04-05', 'purchase', 317),
  (393, '2020-03-21', 'deposit', 739),
  (393, '2020-01-30', 'deposit', 541),
  (393, '2020-03-01', 'deposit', 720),
  (393, '2020-04-10', 'purchase', 476),
  (393, '2020-03-02', 'withdrawal', 310),
  (115, '2020-02-01', 'purchase', 400),
  (115, '2020-03-03', 'deposit', 747),
  (115, '2020-03-07', 'deposit', 672),
  (115, '2020-04-06', 'deposit', 20),
  (115, '2020-02-08', 'withdrawal', 196),
  (115, '2020-03-30', 'deposit', 361),
  (115, '2020-02-25', 'purchase', 393),
  (115, '2020-03-28', 'purchase', 51),
  (115, '2020-04-06', 'withdrawal', 945),
  (150, '2020-02-19', 'purchase', 264),
  (150, '2020-01-05', 'withdrawal', 715),
  (150, '2020-03-24', 'withdrawal', 807),
  (150, '2020-02-03', 'deposit', 63),
  (150, '2020-04-01', 'withdrawal', 825),
  (150, '2020-02-28', 'deposit', 206),
  (150, '2020-03-20', 'deposit', 715),
  (150, '2020-02-09', 'purchase', 546),
  (150, '2020-01-15', 'withdrawal', 869),
  (150, '2020-02-12', 'purchase', 371),
  (150, '2020-01-18', 'withdrawal', 176),
  (150, '2020-01-22', 'deposit', 166),
  (150, '2020-01-27', 'deposit', 925),
  (275, '2020-04-13', 'deposit', 934),
  (275, '2020-03-28', 'purchase', 768),
  (275, '2020-03-19', 'withdrawal', 869),
  (275, '2020-03-25', 'purchase', 473),
  (275, '2020-04-07', 'withdrawal', 643),
  (275, '2020-04-22', 'withdrawal', 445),
  (275, '2020-04-11', 'purchase', 676),
  (275, '2020-02-27', 'withdrawal', 281),
  (275, '2020-03-02', 'deposit', 127),
  (275, '2020-03-14', 'deposit', 600),
  (275, '2020-02-16', 'withdrawal', 918),
  (275, '2020-04-09', 'withdrawal', 220),
  (275, '2020-03-05', 'deposit', 377),
  (275, '2020-02-10', 'purchase', 390),
  (275, '2020-02-24', 'deposit', 53),
  (275, '2020-03-06', 'deposit', 763),
  (275, '2020-03-01', 'withdrawal', 481),
  (275, '2020-03-30', 'withdrawal', 924),
  (275, '2020-04-23', 'deposit', 854),
  (438, '2020-03-08', 'withdrawal', 802),
  (438, '2020-02-01', 'purchase', 93),
  (438, '2020-01-31', 'deposit', 957),
  (438, '2020-02-12', 'withdrawal', 159),
  (438, '2020-02-26', 'deposit', 858),
  (438, '2020-01-20', 'withdrawal', 538),
  (438, '2020-03-07', 'deposit', 273),
  (438, '2020-02-13', 'deposit', 890),
  (438, '2020-01-12', 'deposit', 705),
  (438, '2020-01-13', 'withdrawal', 68),
  (438, '2020-03-25', 'purchase', 861),
  (153, '2020-03-23', 'deposit', 683),
  (153, '2020-03-27', 'purchase', 178),
  (153, '2020-01-26', 'withdrawal', 703),
  (153, '2020-02-07', 'deposit', 664),
  (153, '2020-03-31', 'purchase', 845),
  (153, '2020-03-10', 'deposit', 378),
  (153, '2020-02-01', 'purchase', 302),
  (153, '2020-02-24', 'deposit', 353),
  (153, '2020-02-20', 'deposit', 959),
  (153, '2020-02-25', 'withdrawal', 107),
  (153, '2020-02-15', 'deposit', 752),
  (153, '2020-01-22', 'purchase', 987),
  (153, '2020-02-06', 'withdrawal', 181),
  (153, '2020-02-08', 'purchase', 578),
  (153, '2020-03-21', 'purchase', 274),
  (153, '2020-01-22', 'withdrawal', 587),
  (153, '2020-01-23', 'withdrawal', 431),
  (153, '2020-02-26', 'purchase', 382),
  (153, '2020-03-07', 'purchase', 683),
  (316, '2020-02-25', 'purchase', 305),
  (316, '2020-02-17', 'purchase', 851),
  (316, '2020-03-12', 'purchase', 208),
  (316, '2020-02-06', 'purchase', 663),
  (316, '2020-02-27', 'purchase', 848),
  (316, '2020-03-09', 'deposit', 264),
  (316, '2020-03-31', 'purchase', 872),
  (349, '2020-03-10', 'withdrawal', 818),
  (349, '2020-02-08', 'deposit', 362),
  (349, '2020-02-01', 'deposit', 498),
  (349, '2020-01-18', 'purchase', 981),
  (349, '2020-02-26', 'withdrawal', 885),
  (349, '2020-04-14', 'deposit', 545),
  (349, '2020-03-02', 'deposit', 49),
  (349, '2020-02-15', 'purchase', 171),
  (349, '2020-03-27', 'deposit', 881),
  (349, '2020-01-31', 'withdrawal', 436),
  (349, '2020-04-03', 'deposit', 110),
  (349, '2020-03-28', 'deposit', 665),
  (349, '2020-03-03', 'deposit', 747),
  (349, '2020-03-20', 'deposit', 934),
  (349, '2020-03-27', 'withdrawal', 109),
  (77, '2020-03-07', 'deposit', 92),
  (77, '2020-02-19', 'deposit', 381),
  (77, '2020-03-08', 'withdrawal', 412),
  (77, '2020-03-01', 'deposit', 616),
  (474, '2020-02-08', 'withdrawal', 789),
  (474, '2020-03-08', 'withdrawal', 398),
  (224, '2020-02-22', 'purchase', 336),
  (224, '2020-03-26', 'deposit', 486),
  (224, '2020-04-04', 'deposit', 212),
  (224, '2020-03-28', 'purchase', 910),
  (224, '2020-03-12', 'withdrawal', 907),
  (224, '2020-02-14', 'purchase', 765),
  (224, '2020-02-13', 'purchase', 491),
  (224, '2020-02-20', 'deposit', 899),
  (224, '2020-03-31', 'withdrawal', 927),
  (224, '2020-03-13', 'deposit', 883),
  (214, '2020-02-21', 'withdrawal', 626),
  (214, '2020-01-20', 'deposit', 191),
  (214, '2020-01-21', 'purchase', 940),
  (214, '2020-01-31', 'purchase', 21),
  (214, '2020-03-04', 'deposit', 116),
  (214, '2020-02-22', 'withdrawal', 233),
  (214, '2020-02-23', 'deposit', 438),
  (214, '2020-04-10', 'deposit', 347),
  (214, '2020-03-02', 'deposit', 698),
  (214, '2020-04-06', 'purchase', 546),
  (214, '2020-03-05', 'deposit', 780),
  (214, '2020-02-12', 'withdrawal', 645),
  (214, '2020-04-07', 'deposit', 918),
  (254, '2020-03-31', 'deposit', 734),
  (254, '2020-02-02', 'purchase', 482),
  (254, '2020-02-23', 'purchase', 931),
  (254, '2020-03-27', 'deposit', 961),
  (254, '2020-03-14', 'withdrawal', 782),
  (254, '2020-02-08', 'withdrawal', 971),
  (254, '2020-03-28', 'deposit', 778),
  (254, '2020-01-27', 'purchase', 820),
  (254, '2020-03-18', 'deposit', 590),
  (254, '2020-03-29', 'deposit', 17),
  (254, '2020-02-10', 'withdrawal', 314),
  (254, '2020-02-27', 'purchase', 221),
  (254, '2020-03-30', 'purchase', 5),
  (189, '2020-01-12', 'deposit', 373),
  (189, '2020-03-17', 'purchase', 726),
  (189, '2020-03-18', 'withdrawal', 462),
  (189, '2020-01-30', 'purchase', 956),
  (189, '2020-02-03', 'withdrawal', 870),
  (189, '2020-03-22', 'purchase', 718),
  (189, '2020-02-06', 'purchase', 393),
  (189, '2020-01-22', 'deposit', 302),
  (189, '2020-01-27', 'withdrawal', 861);

select *
from data_bank.customer_transactions
order by customer_id, txn_date;

Questions Sets

A. Customer Nodes Exploration

#1 How many unique nodes are there on the Data Bank system?


select 
count(distinct node_id) as unique_nodes 
from data_bank.customer_nodes

#2 What is the number of nodes per region?


select 
region_id, count(distinct node_id) as npr 
from data_bank.customer_nodes
group by region_id
order by region_id;

#3 How many customers are allocated to each region?


select 
region_id, count(distinct customer_id) as cpr 
from data_bank.customer_nodes
group by region_id
order by region_id;

#4 How many days on average are customers reallocated to a different node?


with node_days as (
    select
        customer_id,
        node_id,
        end_date - start_date as days_in_node
    from data_bank.customer_nodes
    where end_date != '9999-12-31'
),

total_node_days as (
    select
        customer_id,
        node_id,
        sum(days_in_node) as total_days_in_node
    from node_days
    group by
        customer_id,
        node_id
)

select
    round(avg(total_days_in_node)) as avg_reallocation_days
from total_node_days;

#5 What is the median, 80th and 95th percentile for this same reallocation days metric for each region?


with node_days as (
    select
        customer_id,
        region_id,
        node_id,
        end_date - start_date as days_in_node
    from data_bank.customer_nodes
    where end_date != '9999-12-31'
)

select
    r.region_name,
    round(median(nd.days_in_node), 2) as median_reallocation_days,
    round(quantile_cont(nd.days_in_node, 0.80), 2) as p80_reallocation_days,
    round(quantile_cont(nd.days_in_node, 0.95), 2) as p95_reallocation_days
from node_days nd
join data_bank.regions r
    on nd.region_id = r.region_id
group by
    r.region_name
order by
    r.region_name;

B. Customer Transactions

#1 What is the unique count and total amount for each transaction type?


select 
  distinct txn_type, 
  count(distinct customer_id) as unique_customers, 
  sum(txn_amount) as total_amt
from data_bank.customer_transactions
group by txn_type
order by txn_type

#2 What is the average total historical deposit counts and amounts for all customers?


with customers as (
    select distinct customer_id
    from data_bank.customer_nodes
),

customer_deposits as (
    select
        customer_id,
        count(*) as deposit_count,
        sum(txn_amount) as deposit_amount
    from data_bank.customer_transactions
    where txn_type = 'deposit'
    group by customer_id
)

select
    round(avg(coalesce(cd.deposit_count, 0)), 0) as avg_deposit_count,
    round(avg(coalesce(cd.deposit_amount, 0)), 2) as avg_deposit_amount
from customers c
left join customer_deposits cd
    on c.customer_id = cd.customer_id;

#3 For each month - how many Data Bank customers make more than 1 deposit and either 1 purchase or 1 withdrawal in a single month?


with monthly_transactions as (
    select
        customer_id,
        extract(month from txn_date) as month,
        sum(case when txn_type = 'deposit' then 1 else 0 end) as deposit_count,
        sum(case when txn_type = 'purchase' then 1 else 0 end) as purchase_count,
        sum(case when txn_type = 'withdrawal' then 1 else 0 end) as withdrawal_count
    from data_bank.customer_transactions
    group by
        customer_id,
        extract(month from txn_date)
)

select
    month,
    count(*) as customer_count
from monthly_transactions
where deposit_count > 1
  and (purchase_count >= 1 or withdrawal_count >= 1)
group by month
order by month;

#4 What is the closing balance for each customer at the end of the month?


with monthly_transactions as (
    select
        customer_id,
        cast(date_trunc('month', txn_date) as date) as month,
        sum(
            case
                when txn_type = 'deposit' then txn_amount
                when txn_type in ('purchase', 'withdrawal') then -txn_amount
            end
        ) as monthly_balance
    from data_bank.customer_transactions
    group by
        customer_id,
        cast(date_trunc('month', txn_date) as date)
)

select
    customer_id,
    month,
    sum(monthly_balance) over (
        partition by customer_id
        order by month
        rows between unbounded preceding and current row
    ) as closing_balance
from monthly_transactions
order by
    customer_id,
    month;

#5 What is the percentage of customers who increase their closing balance by more than 5%?


with customer_region as (
    select distinct
        cn.customer_id,
        r.region_name
    from data_bank.customer_nodes cn
    join data_bank.regions r
        on cn.region_id = r.region_id
),

monthly_transactions as (
    select
        customer_id,
        date_trunc('month', txn_date)::date as month,
        sum(
            case
                when txn_type = 'deposit' then txn_amount
                when txn_type in ('purchase', 'withdrawal') then -txn_amount
            end
        ) as monthly_balance
    from data_bank.customer_transactions
    group by
        customer_id,
        date_trunc('month', txn_date)::date
),

closing_balance as (
    select
        customer_id,
        month,
        sum(monthly_balance) over (
            partition by customer_id
            order by month
        ) as closing_balance
    from monthly_transactions
),

balance_change as (
    select
        customer_id,
        month,
        closing_balance,
        lag(closing_balance) over (
            partition by customer_id
            order by month
        ) as previous_closing_balance
    from closing_balance
),

customers_increase as (
    select distinct
        customer_id
    from balance_change
    where previous_closing_balance > 0
      and closing_balance > previous_closing_balance * 1.05
),

region_result as (
    select
        cr.region_name,
        count(ci.customer_id) as customers_increased,
        count(cr.customer_id) as total_customers
    from customer_region cr
    left join customers_increase ci
        on cr.customer_id = ci.customer_id
    group by cr.region_name
),

final_result as (
    select
        region_name,
        round(
            100.0 * customers_increased / total_customers,
            2
        ) as percentage_customers
    from region_result

    union all

    select
        'Total' as region_name,
        round(
            100.0 * count(distinct ci.customer_id)
            / count(distinct cr.customer_id),
            2
        ) as percentage_customers
    from customer_region cr
    left join customers_increase ci
        on cr.customer_id = ci.customer_id
)

select
    region_name,
    percentage_customers
from final_result
order by
    case when region_name = 'Total' then 1 else 0 end,
    region_name;

C. Data Allocation Challenge

To test out a few different hypotheses - the Data Bank team wants to run an experiment where different groups of customers would be allocated data using 3 different options:

Option 1: data is allocated based off the amount of money at the end of the previous month
Option 2: data is allocated on the average amount of money kept in the account in the previous 30 days
Option 3: data is updated real-time

For this multi-part challenge question - you have been requested to generate the following data elements to help the Data Bank team estimate how much data will need to be provisioned for each option:

running customer balance column that includes the impact each transaction
customer balance at the end of each month
minimum, average and maximum values of the running balance for each customer

Using all of the data available - how much data would have been required for each option on a monthly basis?

SOLUTION

This is the one I was dreading honestly. Everything before this was straightforward, join here, group by there. This one needed actual thinking before typing a single line.

Approach

Took me a while to even frame the problem right. Three options, three different philosophies basically. Do you check balance once a month, do you average it out, or do you track it every single transaction. Before writing anything I just sat and wrote out on paper what a running balance even means here. Deposits add, purchases and withdrawals subtract. Sounds obvious now, wasnt obvious at 11pm.


with running_balance as (
    select
        customer_id,
        txn_date,
        date_trunc('month', txn_date)::date as month,
        sum(
            case
                when txn_type = 'deposit' then txn_amount
                when txn_type in ('purchase', 'withdrawal') then -txn_amount
            end
        ) over (
            partition by customer_id
            order by txn_date
            rows between unbounded preceding and current row
        ) as running_balance
    from data_bank.customer_transactions
),

month_end_balance as (
    select customer_id, month, running_balance as closing_balance
    from (
        select
            customer_id, month, running_balance,
            row_number() over (partition by customer_id, month order by txn_date desc) as rn
        from running_balance
    ) t
    where rn = 1
),

customer_avg_balance as (
    select customer_id, avg(running_balance) as avg_running_balance
    from running_balance
    group by customer_id
),

option_1 as (
    select month, sum(greatest(closing_balance, 0)) as option_1_data
    from month_end_balance
    group by month
),

option_2 as (
    select rb.month, sum(greatest(cab.avg_running_balance, 0)) as option_2_data
    from (select distinct customer_id, month from running_balance) rb
    join customer_avg_balance cab on rb.customer_id = cab.customer_id
    group by rb.month
),

option_3 as (
    select month, sum(greatest(running_balance, 0)) as option_3_data
    from running_balance
    group by month
)

select
    o1.month,
    round(o1.option_1_data, 2) as option_1_data,
    round(o2.option_2_data, 2) as option_2_data,
    round(o3.option_3_data, 2) as option_3_data
from option_1 o1
join option_2 o2 on o1.month = o2.month
join option_3 o3 on o1.month = o3.month
order by o1.month;

Breaking this down, CTE by CTE

running_balance – this is doing the heavy lifting honestly, everything else just reads off this. Runs a cumulative sum per customer so at any transaction you know exactly where their balance stood.

month_end_balance – grabs the last transaction of each month per customer. Thats your Option 1 number. Simple snapshot, once a month, done.

customer_avg_balance – averages the running balance across the whole history for each customer. Option 2 lives here. Smoother number, not tied to one single moment.

option_1, option_2, option_3 – each just sums up the relevant balance across all customers, per month. Used greatest(x, 0) throughout because negative balances dont need negative storage, thats not how data allocation works in the real world either.

What I actually took away from this

Option 1 is cheap and lazy, once a month, close enough for a rough estimate. Option 2 costs a bit more to compute but gives you something more honest, reflects what customers actually keep in their account rather than one snapshot day. Option 3 is the expensive one. Recalculates after every transaction. Most accurate by far but also the most demanding on infra.

Real lesson here wasnt the SQL. Was realizing accuracy has a cost attached to it and someone has to decide if thats a cost worth paying. More frequent updates, more data processed, simple as that. This is basically every infra decision I've watched people make in real companies, not just a case study exercise. Felt oddly familiar writing this one out.

D. Extra Challenge

Data Bank wants to try another option which is a bit more difficult to implement - they want to calculate data growth using an interest calculation, just like in a traditional savings account you might have with a bank.

If the annual interest rate is set at 6% and the Data Bank team wants to reward its customers by increasing their data allocation based off the interest calculated on a daily basis at the end of each day, how much data would be required for this option on a monthly basis?

Special notes:

Data Bank wants an initial calculation which does not allow for compounding interest, however they may also be interested in a daily compounding interest calculation so you can try to perform this calculation if you have the stamina!

SOLUTION

This is the bonus round I mentioned earlier. Wasnt planning to attempt it honestly, then figured whats the point of doing a case study halfway. So here we are, interest calculations, like an actual savings account. Data Bank wants the data allocation to grow the same way money grows in a bank, based on interest earned daily. 6% annual, calculated every single day, then added on top of the customer's data allocation at end of day.

First pass, I kept it simple. Simple interest, meaning todays interest doesnt get folded back in to earn more interest tomorrow. That comes later, and yes it comes later in this post too.

Calculation approach

Annual rate is 6%, so daily rate is just that divided across the year.


daily interest rate = 6% / 365

For every customer, every day:


daily interest = daily closing balance × (6% / 365)

Add all of that up across a month and thats your extra data requirement. Sounds simple written like this. Took me three attempts to get the query right though.

SQL Query


with daily_transactions as (
    select
        customer_id,
        txn_date,
        sum(
            case
                when txn_type = 'deposit' then txn_amount
                when txn_type in ('purchase', 'withdrawal') then -txn_amount
            end
        ) as daily_net_change
    from data_bank.customer_transactions
    group by customer_id, txn_date
),

daily_balance as (
    select
        customer_id,
        txn_date,
        sum(daily_net_change) over (
            partition by customer_id
            order by txn_date
            rows between unbounded preceding and current row
        ) as closing_balance
    from daily_transactions
),

daily_interest as (
    select
        customer_id,
        txn_date,
        closing_balance,
        greatest(closing_balance, 0) * (0.06 / 365) as daily_interest
    from daily_balance
),

monthly_interest as (
    select
        date_trunc('month', txn_date)::date as month,
        sum(daily_interest) as interest_data
    from daily_interest
    group by date_trunc('month', txn_date)::date
)

select
    month,
    round(interest_data, 2) as additional_data_required
from monthly_interest
order by month;

Breaking this down

daily_transactions – customers can have more than one transaction on the same day, deposit in the morning, purchase in the evening, whatever. So first thing, squash all of that into a single net number per customer per day. Learned this the hard way, forgot to do this step on my first attempt and the numbers looked wrong for no obvious reason. Classic.

daily_balance – running total again, same idea as before, cumulative sum but this time at daily grain instead of per transaction.

daily_interest – this is where the actual 6% comes in. Balance times daily rate. Used greatest(closing_balance, 0) again here, same logic as before, dont want customers who overdrew their account somehow generating negative data. Doesnt make sense in the real world either.

monthly_interest – add up every customer's daily interest across the whole month. Thats your final number.

Monthly Data Requirement

And what about compounding

So the above is simple interest, thats what was asked for first. Interest earned today just sits there, doesnt go on to earn more interest tomorrow.

Compounding is a different beast though. Every day the balance itself updates:


new balance = previous balance × (1 + 0.06 / 365)

Didnt fully build this one out, ran out of steam honestly. But conceptually its not that different, just means each day's balance calculation needs to feed into the next day instead of referencing the original transaction balance. Interest earning interest on interest. Small difference on paper, adds up more than you'd expect over a full year.

What I took away from this one

This was the point where the case study stopped being a SQL exercise and started feeling like an actual finance problem. Balance isnt just balance anymore, its balance plus time plus a rate. Simple interest gets you a reasonable estimate without too much pain. Compounding is more accurate, more real, but also a genuinely harder problem to model well.

Kind of liked that this challenge existed honestly. Most of the earlier questions were "write a query that answers X." This one was closer to "think like the business would think." Different muscle entirely. Took longer to sit with than to actually write.

Extension Request

The Data Bank team wants you to use the outputs generated from the above sections to create a quick Powerpoint presentation which will be used as marketing materials for both external investors who might want to buy Data Bank shares and new prospective customers who might want to bank with Data Bank.

Using the outputs generated from the customer node questions, generate a few headline insights which Data Bank might use to market it’s world-leading security features to potential investors and customers.
With the transaction analysis - prepare a 1 page presentation slide which contains all the relevant information about the various options for the data provisioning so the Data Bank management team can make an informed decision.

SOLUTION

Created using ChatGPT using our data

2 Cents

Went in thinking this was a plain banking case study. Came out thinking it was a data infrastructure problem wearing a banking costume. Took me longer than I'd like to admit to notice that.

Biggest thing I learned, SQL was never the hard part here. The hard part was figuring out what the number actually represents and what decision it supports.

First trap. A customer isnt tied to one node forever, they move around. So counting rows in customer_nodes tells you nothing about actual customer count. Needed count(distinct customer_id), not count(*). Small syntax difference, completely different answer to the business.

The reallocation question broke my brain a little. "How many days on average are customers reallocated to a different node" sounds like one question. Turned out to be four, duration per allocation, gap between allocations, average per customer, average per customer-node pair. All valid SQL. All different answers. Good reminder that a query can run perfectly and still be wrong. Not syntax wrong, business wrong. Worse kind honestly, doesnt throw an error, just confidently gives you the wrong thing.

Running balance unlocked everything after it. Deposit positive, purchase and withdrawal negative, cumulative sum on top. Sounds like nothing. Once you have it though, closing balances, active customers, infra sizing, all just fall out of it.

"All customers" is doing a lot of quiet work in one question. Include only customers who deposited and you've dropped zero-deposit customers from your denominator without meaning to. The denominator is part of the metric. Not a technicality.

Best part of the whole case, data provisioning. Three ways to allocate storage off balance. Monthly snapshot, cheap and simple, doesnt react mid month. Average balance, more honest, costs more to compute. Real time, most accurate, most expensive, recalculates on literally every transaction. Not really about picking a winner, more about where you sit on a complexity vs responsiveness spectrum. Felt like a genuinely real decision, not a textbook one.

Interest round pushed it further. 6% annual, calculated daily, and the question stops being "whats the balance" and becomes "how does this balance keep moving over time." Then compounding gets added on top and suddenly todays allocation feeds tomorrow's calculation. Simple business ask, meaningfully harder model underneath. Same pattern as everything else in this case honestly.

What it actually taught me. Before this, SQL to me was select, filter, group, aggregate, done. Now I'd add, know your grain, know your population, know why you picked sum over count over average, and remember the query isnt the finish line. The real question after any output is what can someone actually do with this now.

If I had to leave this with one line:

Good SQL gets you a number. Good analytics makes sure its the right number and connects it to a decision someone can actually make.

That's basically the whole case study, condensed.