Temporal
Temporal refers to anything related to time. In the context of databases, data warehousing, and analytics, "temporal" generally describes aspects that change or evolve over time, or that are connected to the tracking of time.
Examples
Temporal Data: Data that represents a point or duration in time. This can include:
- Timestamps: A specific point in time (e.g., "2020-10-15 10:30:00").
- Dates: A particular day, month, or year (e.g., "2020-10-15").
- Time Ranges: A start and end date (e.g., "2020-10-01 to 2020-10-15").
Temporal Dimension: A dimension (like dim_date or dim_time) that stores and manages information about time. It's used in queries to analyze data based on time intervals (e.g., sales per month, inventory levels over time).
Temporal Relationships: These are relationships between entities that change over time. For example, in customer data, an individual might change their address, and you may want to track how that address evolves over time.
Temporal Analysis: Analyzing how things change over time, such as:
- Trend Analysis: How sales grow or decline over time.
- Duration Analysis: How long an order stays in each processing step.
- Time-Series Analysis: Data points collected or recorded at specific time intervals to understand patterns (e.g., daily stock prices).
Temporal Context is often important when we talk about step dimensions, slowly changing dimensions (SCDs), or any type of analysis that looks at the evolution of data over time.