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.

When you’re building a data warehouse, the way you model your data can make the difference between fast, intuitive analytics and a never-ending maze of joins. Two of the most common data modeling approaches are Star Schema and Snowflake Schema. Both serve the same purpose—structuring your data to support reporting and analysis—but they differ in design, performance, and usability.