01
গল্প — কেন Aggregate?
১৫ মিনিট
E-commerce-এ ১০ লাখ order। CEO জিজ্ঞেস করে: মোট order কত? Total sales? Average? Highest? Lowest?
1,000,000 rows → Too much data → Business Q → Summary Needed → Aggregate Functions
জিজ্ঞেস
CEO কি ১০ লাখ row দেখতে চায়? নাকি ছোট summary report?
Aggregate Functions অনেক row নিয়ে একটি summary value তৈরি করে।
02
Real-life Examples
১০ মিনিট
School
Total / Average / Highest / Lowest marks · কতজন student?
Company
Total / Average / Highest / Lowest salary · headcount
Hospital
Patients · average age · max/min age
Bank
Total deposits · avg transaction · max/min
03
Aggregate Function কী?
৮ মিনিট
Aggregate Function = একাধিক row-এর data নিয়ে একটি summary result তৈরি করে।
Many Rows → Aggregate Function → One Summary Value
10 20 30 40 50 → SUM() → 150
10 20 30 40 50 → AVG() → 30
04
৫টি Core Function
৬ মিনিট
AGGREGATE FUNCTIONS
│
┌─────────┬───────┼───────┬─────────┐
↓ ↓ ↓ ↓ ↓
COUNT() SUM() AVG() MAX() MIN()
How many Total Average Highest Lowest
05
employees Dataset
১০ মিনিট
CREATE DATABASE agg_lab;
USE agg_lab;
CREATE TABLE employees (
employee_id INT PRIMARY KEY,
employee_name VARCHAR(80),
department VARCHAR(40),
salary DECIMAL(10,2),
age INT
);
INSERT INTO employees VALUES
(1,'Rahim','IT',50000,25),
(2,'Karim','HR',45000,28),
(3,'Jannat','IT',60000,27),
(4,'Nabila','Sales',40000,24),
(5,'Hasan','IT',55000,30),
(6,'Sumi','HR',48000,26),
(7,'Rafi','Finance',70000,32),
(8,'Nila','Sales',42000,23),
(9,'Imran','IT',58000,29),
(10,'Sara','Finance',65000,31),
(11,'Omar','HR',47000,27),
(12,'Mita','Sales',39000,22),
(13,'Tarek','IT',62000,33),
(14,'Lina','Finance',68000,28),
(15,'Jamal','HR',46000,35),
(16,'Pria','IT',53000,26),
(17,'Shakib','Sales',41000,25),
(18,'Rina','Finance',72000,34),
(19,'Fahim','IT',59000,31),
(20,'Ayesha','HR',44000,29);
২০ জন employee — IT / HR / Sales / Finance।
06
COUNT(*)
৮ মিনিট
Company-তে মোট কতজন employee?
SELECT COUNT(*) AS total_employees
FROM employees;
total_employees
20
COUNT(*) = কয়টি row আছে।
07
COUNT(column) vs COUNT(*)
১০ মিনিট
salary: 50000, 60000, NULL, 45000
COUNT(*) → 4 (সব row)
COUNT(salary) → 3 (শুধু non-NULL)
SELECT COUNT(salary) FROM employees;
NULL ≠ 0। COUNT(column) NULL বাদ দেয়।
08
COUNT(DISTINCT)
৮ মিনিট
SELECT COUNT(DISTINCT department) AS total_departments
FROM employees;
total_departments = 4
(IT, HR, Sales, Finance)
09
SUM()
৮ মিনিট
মোট salary খরচ কত?
SELECT SUM(salary) AS total_salary
FROM employees;
50000+45000+… → SUM() → এক মোট সংখ্যা
10
AVG()
৮ মিনিট
40000 + 50000 + 60000 = 150000
COUNT = 3 → AVG = 50000
AVG = SUM(valid values) ÷ COUNT(valid values)
SELECT AVG(salary) AS average_salary
FROM employees;
11
MAX()
১০ মিনিট
SELECT MAX(salary) AS highest_salary
FROM employees;
MAX() শুধু সংখ্যা দেয় — নাম দেয় না।
Optional: কে সর্বোচ্চ পায়?
SELECT employee_name, salary
FROM employees
WHERE salary = (SELECT MAX(salary) FROM employees);
12
MIN()
৬ মিনিট
SELECT MIN(salary) AS lowest_salary
FROM employees;
SELECT employee_name, salary
FROM employees
WHERE salary = (SELECT MIN(salary) FROM employees);
13
Comparison Table
৫ মিনিট
| Function | Meaning | Example |
|---|---|---|
| COUNT() | কতটি | কতজন employee? |
| SUM() | মোট | মোট salary? |
| AVG() | গড় | Average salary? |
| MAX() | সর্বোচ্চ | Highest salary? |
| MIN() | সর্বনিম্ন | Lowest salary? |
COUNT=HOW MANY? · SUM=TOTAL? · AVG=AVERAGE? · MAX=HIGHEST? · MIN=LOWEST?
14
Aggregate vs Normal
৫ মিনিট
Normal Function: Row → Result
Aggregate: Rows → One Summary Result
15
Multiple Aggregates (no GROUP BY)
৮ মিনিট
SELECT
COUNT(*) AS total_employees,
SUM(salary) AS total_salary,
AVG(salary) AS average_salary,
MAX(salary) AS highest_salary,
MIN(salary) AS lowest_salary
FROM employees;
20 Employees → ONE BIG GROUP → ONE SUMMARY ROW
16
GROUP BY + COUNT
১০ মিনিট
GROUP BY = কোন কলাম অনুযায়ী আলাদা আলাদা দল বানাও।
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;
All Employees → GROUP BY department
→ IT / HR / Sales / Finance groups → COUNT each
17
GROUP BY + SUM
৬ মিনিট
SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department;
18
GROUP BY + AVG
৬ মিনিট
SELECT department, AVG(salary) AS average_salary
FROM employees
GROUP BY department;
19
GROUP BY + MAX
৫ মিনিট
SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department;
প্রতি department-এর মধ্যে highest।
20
GROUP BY + MIN
৫ মিনিট
SELECT department, MIN(salary) AS lowest_salary
FROM employees
GROUP BY department;
21
Full Department Report
১০ মিনিট
SELECT
department,
COUNT(*) AS employee_count,
SUM(salary) AS total_salary,
AVG(salary) AS average_salary,
MAX(salary) AS highest_salary,
MIN(salary) AS lowest_salary
FROM employees
GROUP BY department;
Live demo: এক query-তে পুরো department dashboard।
22
Aggregate + WHERE
৮ মিনিট
SELECT AVG(salary) AS average_salary
FROM employees
WHERE department = 'IT';
FROM → WHERE → Filtered Rows → AVG → Result
SELECT department, AVG(salary)
FROM employees
WHERE age > 25
GROUP BY department;
23
Aggregate + HAVING
৮ মিনিট
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;
Rows → WHERE → GROUP BY → COUNT → HAVING → Final Groups
WHERE = row filter · HAVING = group filter
24
WHERE vs HAVING
৮ মিনিট
| WHERE | HAVING |
|---|---|
| Row filter | Group filter |
| Before grouping | After grouping |
| Individual records | Summary groups |
| Usually before GROUP BY | After GROUP BY |
WHERE
salary > 50000
HAVING
AVG(salary) > 50000
25
NULL + Aggregates
১০ মিনিট
50000, 60000, NULL, 40000
COUNT(*) → 4
COUNT(salary) → 3
SUM / AVG / MAX / MIN → ignore NULL
NULL শূন্য নয় — বেশিরভাগ aggregate NULL বাদ দেয়।
26
DISTINCT + Aggregate
৫ মিনিট
SELECT COUNT(DISTINCT department) FROM employees;
Unique customers / cities / categories count করতে ব্যবহার।
27
Aggregate + ORDER BY / LIMIT
৮ মিনিট
SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
ORDER BY total_salary DESC
LIMIT 1;
GROUP → SUM → ORDER BY → LIMIT 1 → top department
28
E-commerce KPIs
১০ মিনিট
CREATE TABLE orders (
order_id INT, customer_name VARCHAR(80),
city VARCHAR(80), category VARCHAR(80),
product VARCHAR(80), quantity INT,
price DECIMAL(10,2), order_date DATE
);
-- insert sample rows in class
SELECT COUNT(*) FROM orders;
SELECT SUM(quantity * price) FROM orders;
SELECT AVG(quantity * price) FROM orders;
SELECT MAX(quantity * price) FROM orders;
SELECT MIN(quantity * price) FROM orders;
29
E-com + GROUP BY
১০ মিনিট
SELECT category, SUM(quantity * price) AS total_sales
FROM orders GROUP BY category;
SELECT city, COUNT(*) AS total_orders
FROM orders GROUP BY city;
SELECT city, AVG(quantity * price) AS aov
FROM orders GROUP BY city;
SELECT category, MAX(quantity * price) AS highest_order
FROM orders GROUP BY category;
30
Business Questions → SQL (১৫)
১২ মিনিট
BQ 1 — Which city has most orders?
GROUP BY city ORDER BY COUNT(*) DESC LIMIT 1
BQ 2 — Total sales overall?
SELECT SUM(quantity*price) FROM orders
BQ 3 — Average order value?
SELECT AVG(quantity*price) FROM orders
BQ 4 — Highest order amount?
SELECT MAX(quantity*price) FROM orders
BQ 5 — Lowest order amount?
SELECT MIN(quantity*price) FROM orders
BQ 6 — Sales by category?
GROUP BY category SUM(qty*price)
BQ 7 — Orders by city?
GROUP BY city COUNT(*)
BQ 8 — Avg order by city?
GROUP BY city AVG(qty*price)
BQ 9 — Max order by category?
GROUP BY category MAX(qty*price)
BQ 10 — Unique customers?
COUNT(DISTINCT customer_name)
BQ 11 — Categories with sales > 100000?
GROUP BY category HAVING SUM(...)>100000
BQ 12 — Cities with >20 orders?
HAVING COUNT(*)>20
BQ 13 — Top category by sales?
ORDER BY total_sales DESC LIMIT 1
BQ 14 — Total Electronics sales?
WHERE category='Electronics' SUM(qty*price)
BQ 15 — Count orders Dhaka?
WHERE city='Dhaka' COUNT(*)
31
Aggregation Animation
৮ মিনিট
RAW: IT 50k, HR 40k, IT 60k, Sales 45k, IT 55k, HR 50k
↓ GROUP BY department
IT: 50k,60k,55k | HR: 40k,50k | Sales: 45k
↓ SUM / AVG / COUNT / MAX / MIN
SUMMARY REPORT
32
Guess the Output
১০ মিনিট
Guess: SELECT COUNT(*) FROM employees (20 rows)?
20
Guess: SUM of 40k+50k+60k?
150000
Guess: AVG of 40k+50k+60k?
50000
Guess: MAX of 40k,50k,60k?
60000
Guess: MIN of 40k,50k,60k?
40000
Guess: COUNT(*) with 1 NULL salary row among 4?
4
Guess: COUNT(salary) same data?
3
Guess: GROUP BY dept → how many result rows (4 depts)?
4
Guess: WHERE dept='IT' then AVG — rows?
শুধু IT filtered then AVG
Guess: HAVING COUNT(*)>5 — filters what?
groups, not raw rows
33
Choose the Function
৮ মিনিট
How many customers?
COUNT()
Total sales?
SUM()
Average salary?
AVG()
Highest price?
MAX()
Lowest price?
MIN()
Unique cities?
COUNT(DISTINCT city)
Per-department headcount?
COUNT(*) + GROUP BY department
Departments with >5 people?
HAVING COUNT(*)>5
IT average only?
WHERE department='IT' + AVG
Top department by cost?
SUM + GROUP BY + ORDER BY DESC LIMIT 1
Monthly sales?
SUM + GROUP BY YEAR/MONTH
Ignore NULL salaries in count?
COUNT(salary)
All rows including NULL cols?
COUNT(*)
Total of price×qty?
SUM(quantity*price)
Filter groups by avg>50k?
HAVING AVG(salary)>50000
34
Common Mistakes (১৫)
১০ মিনিট
- SUM দিয়ে count
- COUNT দিয়ে money
- AVG ও SUM গুলিয়ে
- GROUP BY ভুলে
- Non-grouped column SELECT
- WHERE vs HAVING গুলিয়ে
- NULL = 0 ভাবা
- COUNT(col) যখন COUNT(*) চাই
- DISTINCT ভুলে
- MAX দিয়ে পুরো row চাওয়া
- MIN ভুল ব্যবহার
- Wrong GROUP column
- Alias না দেওয়া
- Business Q না বুঝে function
- Aggregate সবসময় ১ row — GROUP BY থাকলে নয়
Pattern
WRONG → WHY? → CORRECT
35
Execution Flow
৮ মিনিট
No GROUP BY
FROM→WHERE→ALL ROWS→AGG→ONE SUMMARY
With GROUP BY
FROM→WHERE→GROUP→AGG each→HAVING→RESULT
FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT
36
Aggregate vs GROUP BY
৫ মিনিট
GROUP BY → Creates Groups
Aggregate Function → Calculates Summary inside groups
37
Single vs Multiple Groups
৫ মিনিট
No GROUP BY: All → One Group → One Result
With GROUP BY: IT/HR/Sales/Finance → Multiple Results
38
Industry Use
৬ মিনিট
- Analytics / BI: KPI, sales, dashboards
- Finance: revenue, expense, profit
- HR: headcount, salary cost
- Data Science: feature / behavior summaries
- Data Engineering: ETL batch summaries, DQ counts
39
Mini Project — Employee Dashboard
১২ মিনিট
SELECT department,
COUNT(*) AS employee_count,
SUM(salary) AS total_salary,
AVG(salary) AS average_salary,
MAX(salary) AS highest_salary,
MIN(salary) AS lowest_salary
FROM employees
GROUP BY department
ORDER BY total_salary DESC;
- Highest employee count dept
- Highest salary cost dept
- Highest average salary dept
- Depts with >5 employees
- Depts with avg salary > 50000
40
Advanced Beginner — CASE
৮ মিনিট
SELECT SUM(quantity * price) FROM orders;
SELECT SUM(
CASE WHEN category = 'Electronics'
THEN quantity * price ELSE 0 END
) AS electronics_sales
FROM orders;
SELECT SUM(
CASE WHEN salary > 50000 THEN 1 ELSE 0 END
) AS high_earners
FROM employees;
Conditional aggregation — KPI metric তৈরির ধারণা।
41
Aggregates + Dates
৮ মিনিট
SELECT
YEAR(order_date) AS year,
MONTH(order_date) AS month,
SUM(quantity * price) AS total_sales
FROM orders
GROUP BY YEAR(order_date), MONTH(order_date);
42
Multi-column GROUP BY
৬ মিনিট
SELECT city, category,
SUM(quantity * price) AS total_sales
FROM orders
GROUP BY city, category;
City → Category → Aggregate
কোন city-তে কোন category কত sales?
43
Performance Basics
৫ মিনিট
1 Billion Rows → Filter early → Aggregate smaller set → Better
Index helps filter/join · Avoid extra columns · EXPLAIN peek
44
Interview Top 30
১৫ মিনিট
IV 1. Aggregate function কী?
Many rows → one summary · উদা: COUNT · ফলো: five cores · ইউজ: KPI
IV 2. Five common?
COUNT SUM AVG MAX MIN · ভুল: JOIN · ফলো: example · ইউজ: dashboards
IV 3. COUNT vs COUNT(col)?
* all rows · col non-NULL · ভুল: same · ফলো: NULL · ইউজ: data quality
IV 4. COUNT(*) vs COUNT(DISTINCT)?
rows vs unique values · ভুল: same · ফলো: depts · ইউজ: cardinality
IV 5. SUM?
যোগফল · ভুল: count · ফলো: salary · ইউজ: revenue
IV 6. AVG?
গড় · ভুল: median always · ফলো: formula · ইউজ: AOV
IV 7. MAX?
সর্বোচ্চ মান · ভুল: row · ফলো: subquery · ইউজ: peaks
IV 8. MIN?
সর্বনিম্ন · ভুল: COUNT · ফলো: who · ইউজ: floors
IV 9. NULL effect?
mostly ignored · ভুল: as 0 · ফলো: COUNT · ইউজ: clean data
IV 10. Without GROUP BY?
হ্যাঁ — one group · ভুল: illegal · ফলো: multi-agg · ইউজ: overview
IV 11. With GROUP BY?
হ্যাঁ — per group · ভুল: only WHERE · ফলো: dept · ইউজ: BI
IV 12. WHERE vs HAVING?
row vs group · ভুল: same · ফলো: examples · ইউজ: filters
IV 13. WHERE with aggregate?
সাধারণত না · ভুল: WHERE AVG · ফলো: HAVING · ইউজ: syntax
IV 14. HAVING with aggregate?
হ্যাঁ · ভুল: only WHERE · ফলো: COUNT>5 · ইউজ: thresholds
IV 15. Unique customers?
COUNT(DISTINCT id) · ভুল: COUNT(*) · ফলো: city · ইউজ: CRM
IV 16. Total sales?
SUM(qty*price) · ভুল: COUNT · ফলো: category · ইউজ: finance
IV 17. Average salary?
AVG(salary) · ভুল: SUM/COUNT manual only · ফলো: IT WHERE · ইউজ: HR
IV 18. Highest salary?
MAX(salary) · ভুল: ORDER only · ফলো: name subquery · ইউজ: payroll
IV 19. Lowest salary?
MIN(salary) · ভুল: AVG · ফলো: name · ইউজ: banding
IV 20. Aggregate vs scalar?
many rows vs one value/row · ভুল: same · ফলো: UPPER · ইউজ: teaching
IV 21. GROUP BY vs agg?
split vs calculate · ভুল: synonyms · ফলো: visual · ইউজ: design
IV 22. Multiple aggs together?
হ্যাঁ · ভুল: one only · ফলো: dashboard · ইউজ: reports
IV 23. Expression in SUM?
SUM(qty*price) · ভুল: only columns · ফলো: CASE · ইউজ: metrics
IV 24. COUNT + NULL?
COUNT(col) skips · ভুল: errors · ফলো: * · ইউজ: DQ
IV 25. AVG + NULL?
ignores NULL · ভুল: divides by all · ফলো: formula · ইউজ: stats
IV 26. Top category?
GROUP SUM ORDER LIMIT · ভুল: MAX alone · ফলো: ties · ইউজ: merch
IV 27. Emp per dept?
COUNT GROUP BY dept · ভুল: DISTINCT only · ফলো: HAVING · ইউজ: org
IV 28. Filter groups?
HAVING · ভুল: WHERE AVG · ফলো: >5 · ইউজ: alerts
IV 29. Monthly sales?
GROUP BY YEAR MONTH · ভুল: only WHERE date · ফলো: chart · ইউজ: trends
IV 30. BI dashboards?
KPI tiles from aggs · ভুল: raw dumps · ফলো: refresh · ইউজ: exec
45
MCQ (২৫+)
১২ মিনিট
MCQ 1. Aggregate means? A) sort B) many→summary C) JOIN D) DELETE
B
MCQ 2. How many? A) SUM B) COUNT C) MAX D) MIN
B
MCQ 3. Total money? A) COUNT B) SUM C) AVG D) DISTINCT
B
MCQ 4. Average? A) AVG B) MAX C) MIN D) COUNT
A
MCQ 5. Highest? A) MIN B) MAX C) SUM D) AVG
B
MCQ 6. Lowest? A) MAX B) MIN C) SUM D) COUNT
B
MCQ 7. COUNT(*) with NULL salary? A) skips row B) counts row C) error D) 0
B
MCQ 8. COUNT(salary) NULL? A) counts B) skips C) as 0 D) error
B
MCQ 9. No GROUP BY result rows? A) many B) usually 1 C) 0 D) PK count
B
MCQ 10. GROUP BY dept rows? A) 1 B) #departments C) #employees D) random
B
MCQ 11. Filter rows before agg? A) HAVING B) WHERE C) LIMIT D) ORDER
B
MCQ 12. Filter groups? A) WHERE B) HAVING C) FROM D) JOIN
B
MCQ 13. Unique depts? A) COUNT(*) B) COUNT(DISTINCT department) C) SUM D) MAX
B
MCQ 14. NULL = 0? A) yes B) no C) always D) only SUM
B
MCQ 15. SUM ignores NULL? A) yes B) no C) errors D) converts 0 always
A
MCQ 16. AVG formula? A) SUM/COUNT(valid) B) MAX-MIN C) COUNT only D) DISTINCT
A
MCQ 17. MAX returns name? A) yes B) no — value only C) always ID D) JOIN auto
B
MCQ 18. Multiple aggs OK? A) no B) yes C) only COUNT D) only with JOIN
B
MCQ 19. SUM(qty*price)? A) illegal B) OK expression C) needs VIEW D) needs UNION
B
MCQ 20. Top dept cost? A) MAX only B) SUM+ORDER+LIMIT C) COUNT D) MIN
B
MCQ 21. HAVING COUNT(*)>5? A) row filter B) group filter C) sort D) join
B
MCQ 22. WHERE AVG(salary)? A) usually wrong B) always OK C) required D) synonym HAVING
A
MCQ 23. GROUP BY role? A) calculate B) create groups C) delete D) index
B
MCQ 24. Agg role? A) create groups B) summarize C) primary key D) FK
B
MCQ 25. City+category sales? A) GROUP BY city, category B) WHERE only C) LIMIT D) UNION
A
MCQ 26. Memory: COUNT=?
HOW MANY?
46
Viva
১০ মিনিট
Viva 1. Aggregate কী?
Many rows → summary
Viva 2. পাঁচটি নাম?
COUNT SUM AVG MAX MIN
Viva 3. COUNT(*)?
সব row
Viva 4. COUNT(col)?
non-NULL
Viva 5. SUM?
মোট
Viva 6. AVG?
গড়
Viva 7. MAX/MIN?
সর্বোচ্চ/নিম্ন
Viva 8. NULL?
সাধারণত ignore
Viva 9. GROUP BY ছাড়া?
এক group
Viva 10. GROUP BY সহ?
প্রতি group summary
Viva 11. WHERE?
row filter
Viva 12. HAVING?
group filter
Viva 13. DISTINCT count?
COUNT(DISTINCT …)
Viva 14. Total sales?
SUM(qty*price)
Viva 15. Dept headcount?
COUNT GROUP BY dept
Viva 16. Top dept?
ORDER BY SUM DESC LIMIT 1
Viva 17. Agg vs GROUP?
calc vs split
Viva 18. Multiple aggs?
হ্যাঁ
Viva 19. CASE in SUM?
conditional KPI
Viva 20. Monthly?
GROUP BY year, month
Viva 21. Mistake SUM for count?
use COUNT
Viva 22. MAX name?
subquery/WHERE
Viva 23. BI use?
dashboard KPIs
Viva 24. Performance tip?
filter early
Viva 25. Final model?
raw→agg→group→report
Viva 26. One vs many groups?
no GROUP BY vs with
47
Classroom Exercises (২০)
১২ মিনিট
Ex 1 — Count employees
SELECT COUNT(*) FROM employees
Ex 2 — Sum salaries
SELECT SUM(salary) FROM employees
Ex 3 — Avg salaries
SELECT AVG(salary) FROM employees
Ex 4 — Highest salary
SELECT MAX(salary) FROM employees
Ex 5 — Lowest salary
SELECT MIN(salary) FROM employees
Ex 6 — Count by dept
COUNT(*) GROUP BY department
Ex 7 — Total salary by dept
SUM(salary) GROUP BY department
Ex 8 — Avg by dept
AVG GROUP BY department
Ex 9 — Max by dept
MAX GROUP BY department
Ex 10 — Min by dept
MIN GROUP BY department
Ex 11 — Highest total salary dept
ORDER BY SUM DESC LIMIT 1
Ex 12 — Depts >5 employees
HAVING COUNT(*)>5
Ex 13 — IT average
WHERE department='IT' AVG
Ex 14 — Total salary age>30
WHERE age>30 SUM(salary)
Ex 15 — Unique departments
COUNT(DISTINCT department)
Ex 16 — Sales by city
orders GROUP BY city SUM
Ex 17 — Sales by category
GROUP BY category SUM
Ex 18 — AOV by city
AVG(qty*price) GROUP BY city
Ex 19 — Highest order by category
MAX GROUP BY category
Ex 20 — Cities sales>100000
HAVING SUM>100000
48
Homework Project
বাড়ি
E-commerce Sales Analytics Report
- Overall: COUNT, SUM, AVG, MAX, MIN, unique customers/cities/categories
- Category analysis: orders, sales, avg, max, min
- City analysis: orders, sales, AOV
- HAVING: sales>100000 · cities>20 orders
- Dashboard query: all five aggs + GROUP BY + WHERE/HAVING/ORDER/LIMIT
Hint overall KPIs
SELECT COUNT(*), SUM(qty*price), AVG(...), MAX, MIN, COUNT(DISTINCT …)
Hint category block
GROUP BY category with multi-agg
Hint city block
GROUP BY city
Hint filters
HAVING SUM>100000 / HAVING COUNT(*)>20
49
1-Minute Revision
৩ মিনিট
COUNT → কতটি? SUM → মোট কত?
AVG → গড় কত? MAX → সবচেয়ে বেশি?
MIN → সবচেয়ে কম?
GROUP BY → groups · Aggregate → summary
WHERE → filter rows · HAVING → filter groups
50
Final Memory Map
৫ মিনিট
SQL AGGREGATE
COUNT / SUM / AVG / MAX / MIN
↓
GROUP BY → Create Groups → Aggregate → HAVING → Report
RAW DATA → Many Rows → Need Summary → AGGREGATE
→ (GROUP BY if separate summaries)
→ WHERE / HAVING → ORDER BY / LIMIT → BUSINESS REPORT
Aggregate Function = অনেকগুলো row-এর data নিয়ে summary information তৈরি করে।