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.