What is a common use case for using Databricks?

Using Databricks for performing big data analytics is a prevalent use case because the platform is specifically designed for processing and analyzing large datasets efficiently. Databricks leverages Apache Spark, which is optimized for distributed computing, allowing users to execute complex data processing tasks that would be cumbersome or slow on traditional systems. This capability enables organizations to derive insights from vast amounts of data quickly, making Databricks an ideal choice for big data applications ranging from data transformation to advanced analytics and machine learning workflows.

In contrast, the other suggested use cases do not align with the primary strengths of the Databricks platform. Creating simple spreadsheets typically involves standard spreadsheet software rather than big data solutions. Coding video games requires specialized game development tools and environments that focus on graphics and interactive elements, whereas trading stocks in real-time demands low-latency systems and often different technologies focused on financial transactions and market analysis, rather than the data-centric approach of Databricks.