Business Analytics Course [2026] | Master Business Strategy & Data-Driven Decisions

Lesson 5.2.2 — JOIN, GROUP BY, HAVING

🔗 JOIN — Combine Related Data

JOINs let you bring fields from different tables together — for example, linking sales records to customer profiles or product master data. Choose the right join type for your question.

Common Join Types

INNER JOIN — rows with matching keys on both sides
LEFT JOIN — all left rows, matching right rows (NULL if no match)
RIGHT JOIN — mirror of LEFT JOIN
FULL OUTER JOIN — all rows from both sides

Example — JOIN

-- Sales with customer segment
SELECT s.order_id, s.sales_amount, c.customer_id, c.segment
FROM sales s
LEFT JOIN customers c
  ON s.customer_id = c.customer_id
WHERE s.sales_date >= '2025-01-01';

Tip: LEFT JOIN helps you keep sales even if customer profile is missing.

📊 GROUP BY — Aggregation & Bucketing

Use GROUP BY to aggregate rows into buckets (by product, region, month). Combine with aggregate functions like SUM(), AVG(), COUNT(), MIN(), MAX().

Example — GROUP BY with date_trunc

-- Monthly revenue by region (Postgres syntax)
SELECT date_trunc('month', sales_date) AS month,
       region,
       SUM(sales_amount) AS revenue
FROM sales
GROUP BY month, region
ORDER BY month DESC, revenue DESC;

Tip: Use date_trunc or equivalent to bucket time-series data reliably.

🔍 HAVING — Filter After Aggregation

HAVING filters groups created by GROUP BY (unlike WHERE which filters rows before aggregation).

Example — HAVING

-- Regions with revenue > $500k in the quarter
SELECT region, SUM(sales_amount) AS total_revenue
FROM sales
WHERE sales_date BETWEEN '2025-01-01' AND '2025-03-31'
GROUP BY region
HAVING SUM(sales_amount) > 500000
ORDER BY total_revenue DESC;

Tip: Use HAVING for business rules like “only show segments exceeding threshold.”