Your First SQL Queries — SELECT, FROM, WHERE
How Analysts Pull the Right Data to Answer Real Business Questions
SQL (Structured Query Language) is the lingua franca of data. If you can write a basic query, you can unlock insights hiding in millions of rows of business data.
In this article, you’ll learn:
The core building blocks of a SQL query
How to filter data using business criteria
Real-world scenarios inspired by data-driven companies like Netflix, Amazon, Uber & Salesforce
Practice patterns you can reuse in any analytics role
🔧 The Core Query Pattern
Every beginner SQL query starts with three essential keywords:
SELECT – What do you want to see? (columns, calculated values) FROM – Where is the data stored? (table or view) WHERE – Which rows matter? (filters tied to the business question)
🧱 Know Your Data: Tables, Rows, Columns
Think of a table like a spreadsheet tab — but way more scalable. Each row = one record (customer, order, trip, transaction). Each column = one attribute (city, amount, date).
When analysts combine tables using keys (e.g., Customer_ID), they get the full story: Who bought what, when, where, and for how much.
🎯 Start With a Business Question (Always)
“Show me all customers in Mumbai who spent more than ₹10,000 last quarter.” “Find rides in Bengaluru longer than 30 minutes during peak hours.” “List open sales opportunities over $50K that haven’t been updated in 14+ days.”
Each of these becomes an approachable SQL query. Let’s translate them.
🧩 Real-World-Style Scenarios & SQL Examples
(These are instructional scenarios inspired by how large data-driven companies operate. Use the patterns with your own business data.)
🎬 Scenario 1: Streaming Engagement by City (Netflix-Style)
Business Question: Which users in Hyderabad watched more than 10 hours of content last month?
Tables: ,
Why it matters: Target high-engagement users for early feature rollouts or region-specific promotions.
📦 Scenario 2: High-Value Orders by Region (Amazon-Style)
Business Question: Show all orders over ₹50,000 placed in India during July 2025.
Table:
Why it matters: Identify top-value transactions for premium support, fraud checks, or loyalty programs.
🚗 Scenario 3: Trip Delay Investigation (Uber-Style)
Business Question: Find completed trips in Delhi with ride times > 45 minutes during weekday peak hours (5–8 PM).
Table:
Calculated ride time = .
Why it matters: Ops teams can investigate traffic spikes, routing issues, or driver shortages.
💼 Scenario 4: Stalled High-Value Deals (Salesforce-Style)
Business Question: Which active opportunities over $100K haven’t been updated in 30+ days?
Table:
Why it matters: Sales leadership can intervene before large deals stall or die.
🧠 Understanding the WHERE Clause: Filters That Answer Questions
The WHERE clause is where business logic lives. Here are common patterns:
🧮 Quick Practice Dataset
Imagine a and schema:
Try These:
Q1: All Chennai customers.
Q2: Orders above ₹1 lakh (100,000).
Q3: Enterprise customers with high-value July orders.
🧭 Analyst Mindset Checklist (Before You Query)
Before writing SQL, ask:
Who is the audience? Ops, finance, marketing?
What decision will this data support?
What filters define “in scope”? Dates? Geography? Status? Threshold?
What does “active,” “delivered,” or “churned” mean in business terms?
Which tables store the needed columns? (Schema review saves time!)
🧑🏫 Teaching Tip (For Managers & Educators)
Give new analysts a spreadsheet of sample business questions and have them only write the WHERE clause first in natural language:
“Filter for India + July + Orders > ₹50K + Delivered.”
Then translate to SQL. This reinforces that SQL answers business questions, not just database trivia.
📥 This Week’s Download
💬 Let’s Talk SQL
What’s the first real business question you want to answer with SQL? Drop it below and I’ll help you write the query. 👇
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