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.

As data volumes continue to grow exponentially, traditional SQL engines often struggle to handle massive datasets efficiently. That’s where Apache Spark SQL comes in — a powerful module that combines the scalability of distributed computing with the simplicity of SQL syntax.

When building data warehouses, you’ll often face a classic challenge: how to track changes in dimension data over time. For example, what if a customer moves to a new city? Should you overwrite their old address or keep the historical record?

When working with databases, business analysts often come across two common objects: stored procedures and views. Both play a key role in querying and managing data, but they serve different purposes. Understanding their differences helps analysts write better queries, collaborate effectively with engineers, and choose the right tool for their reporting needs.

CTE (Common Table Expression) is a temporary result set defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement.