🎯 𝐒𝐐𝐋 𝐒𝐞𝐫𝐢𝐞𝐬 | 𝐄𝐩. 𝟖 — 𝐇𝐀𝐕𝐈𝐍𝐆: 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 𝐆𝐫𝐨𝐮𝐩𝐬, 𝐍𝐨𝐭 𝐑𝐨𝐰𝐬

𝐖𝐡𝐲 𝐖𝐇𝐄𝐑𝐄 𝐂𝐚𝐧’𝐭 𝐂𝐚𝐭𝐜𝐡 𝐎𝐯𝐞𝐫𝐬𝐩𝐞𝐧𝐝𝐢𝐧𝐠 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬

🧠 𝐇𝐨𝐨𝐤:
Imagine you’re a Data Analyst at LinkedIn.
You’re reviewing project finances and need to 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐡𝐚𝐭 𝐰𝐞𝐧𝐭 𝐨𝐯𝐞𝐫 𝐛𝐮𝐝𝐠𝐞𝐭.
Seems simple? You just need to compare:
Total employee cost per project vs. Project budget

But here’s the twist — employees work on different projects for different durations, and you need to 𝐩𝐫𝐨𝐫𝐚𝐭𝐞 their annual salaries before summing them up.

And once you’ve calculated total costs per project, you need to 𝐟𝐢𝐥𝐭𝐞𝐫 𝐨𝐧𝐥𝐲 𝐭𝐡𝐨𝐬𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐰𝐡𝐞𝐫𝐞 𝐜𝐨𝐬𝐭𝐬 𝐞𝐱𝐜𝐞𝐞𝐝 𝐭𝐡𝐞 𝐛𝐮𝐝𝐠𝐞𝐭 — That’s when 𝐇𝐀𝐕𝐈𝐍𝐆 becomes your hero. 🚀

💡 𝐂𝐨𝐧𝐜𝐞𝐩𝐭:
The 𝐇𝐀𝐕𝐈𝐍𝐆 clause filters results 𝐚𝐟𝐭𝐞𝐫 𝐚𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧, unlike 𝐖𝐇𝐄𝐑𝐄 , which filters rows 𝐛𝐞𝐟𝐨𝐫𝐞 𝐚𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧.

🧩 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: 𝐑𝐢𝐬𝐤𝐲 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 — https://lnkd.in/e92_5JKx

𝐓𝐚𝐬𝐤: Find projects where total prorated salary cost of assigned employees exceeds the project’s budget.

𝐋𝐨𝐠𝐢𝐜 𝐁𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧:

1️⃣ Calculate how long each project lasted (in days).
2️⃣ Prorate each employee’s annual salary for their duration on that project.
3️⃣ Sum total prorated cost per project.
4️⃣ Compare that total cost with the project’s budget.
5️⃣ Keep only projects where cost > budget → use HAVING.

💻𝐒𝐐𝐋 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧:

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with project_emp as (Select p.title,
p.budget,
(p.end_date — p.start_date)*(e.salary/365.0) as employee_salary_perproject,
e.salary
from
linkedin_projects p join linkedin_emp_projects ep
on p.id=ep.project_id join linkedin_employees e on
e.id=ep.emp_id)

select title,budget,ceil(sum(employee_salary_perproject)) as prorated_employee_expense from project_emp
group by title, budget having
budget(ceil(sum(employee_salary_perproject)) order by title;

✅ 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 → Groups data by project.
✅ 𝐒𝐔𝐌() → Aggregates prorated salary per project.
✅ 𝐇𝐀𝐕𝐈𝐍𝐆 → Filters projects exceeding budget.
✅ 𝐂𝐄𝐈𝐋() → Rounds cost up to the nearest dollar for reporting accuracy.

💬 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭:

The HAVING clause helps financial analysts, PMOs, and operations teams quickly:

➡️ Spot departments exceeding cost limits
➡️ Identify stores or campaigns performing above or below thresholds

This is how SQL turns data into action.

🧩 𝐓𝐫𝐲 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬:

1️⃣ Marketing Campaign Success — https://lnkd.in/eAVZcfQU
2️⃣ Actor Rating Difference Analysis — https://lnkd.in/eAGx8QsG

🚀 𝐂𝐨𝐦𝐢𝐧𝐠 𝐍𝐞𝐱𝐭:

𝐒𝐐𝐋 𝐒𝐞𝐫𝐢𝐞𝐬 | 𝐄𝐩. 𝟗 — 𝐈𝐍𝐍𝐄𝐑 𝐉𝐎𝐈𝐍: 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬 𝐭𝐨 𝐓𝐞𝐥𝐥 𝐚 𝐒𝐭𝐨𝐫𝐲

Have you ever used HAVING to catch an anomaly or threshold breach in real data?