Most companies don’t fail at AI because of models. They fail because of data.

More specifically:

They try to run AI workloads on top of a data warehouse that was never designed for it.

Most analysts start experiment analysis in SQL.

You write a query.
You compare treatment vs control.
You calculate lift.
You declare a winner.

For many well-designed A/B tests, that’s perfectly valid.

When people talk about feature engineering, SQL is often treated as a second-class citizen.

You’ll hear things like:

• “Feature engineering should be done in Python.”
• “SQL is just for data extraction.”
• “Real modeling happens after the data leaves the warehouse.”

If you’ve ever run an A/B test, you’ve probably seen this happen:

• The metrics bounce around every day
• It takes forever to reach significance
• Your A group is magically “different” from your B group
• Stakeholders keep asking, “Is this test done yet?”

If you work with data — in analytics, BI, or data engineering — you’ve probably heard the term dbt (pronounced “dee-bee-tee”). It has become one of the most popular tools in the modern data stack because it empowers analysts to build production-grade data pipelines using just SQL.

A/B testing (or split testing) is one of the most powerful tools in an analyst’s toolbox: it allows you to compare two (or more) versions of a web page, feature, or user experience — and determine which version truly performs better.

When building data-driven solutions — whether dashboards, reports, or analytical pipelines — you often focus on selecting, transforming, or visualizing data. But to make all that work, you need a well-structured database behind the scenes. That’s where Data Definition Language (DDL) comes in.

JSON (JavaScript Object Notation) has become the universal language for exchanging data between applications. Whether you’re pulling data from APIs, storing logs, or dealing with semi-structured data in a data lake — JSON is everywhere.

If you’ve worked with SQL for data analysis, you’ve probably used the SELECT DISTINCT keyword to remove duplicate rows. But at some point, you might come across another term — SELECT UNIQUE.