SQL in industry

1. SQL in industry

Every analyst follows a workflow of exploration, analysis, and communication. SQL powers most of this workflow, and AI helps us execute it more efficiently.

2. SQL in industry

SQL is a popular tool for data analysis in healthcare,

3. SQL in industry

finance,

4. SQL in industry

retail,

5. SQL in industry

government,

6. SQL in industry

and virtually every industry that handles information. Across industries, analysts ask similar types of questions that SQL answers exceptionally well.

7. Asking the right question

Each case starts with a clear question that defines the business problem. We might ask: "How many?" for counting records, "What are the unique values?" for finding distinct items, "Which records meet specific criteria?" for filtering data. Each question maps to a common SQL query the AI can write in seconds. We'll explore some of these in the upcoming videos, but for now, it's helpful to see how different questions and phrases lead to different types of queries.

8. Translate questions into SQL

Knowing which SQL query answers which everyday language question lets us write more precise prompts and verify AI-generated results. For example, if we want to understand "What customer data do we have?", the AI assistant may generate a query with SELECT all and FROM customers. If we ask "Where are our customers from?", the AI assistant is likely to generate the same thing again without the schema information. Our questions will need more specifics if we want the AI assistant to generate a SELECT statement listing only relevant fields. For example, "What country is each customer from?" will likely result with only the name and country fields.

9. Translate questions into SQL

We've already seen examples of this connection in action. When we said "first five products," the AI understood to add the LIMIT keyword to our query. When we ask to see data "as first_name," AI generates the AS keyword for cleaner field names. By tracing the SELECT and FROM clauses back to our words, we can confirm the AI understood us and spot any mismatches, such as extra fields or the wrong table.

10. Your SQL and AI journey

The more complex the question, the more vital this cross-check becomes. As we continue building our SQL knowledge, we'll develop the confidence to verify and modify AI-generated queries to uncover trends and answer questions like: How many customers do we have? What are our most popular products? Which regions have the highest sales? These analytical questions become possible as we learn more SQL capabilities.

11. Let's practice!

Let's see how our prompts translate into SQL.

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