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Data Engine Thinking Data Engine Thinking Samples

Snapshots

Snapshots capture the state of data at specific points in time, providing a frozen view of entities for a given moment. Unlike timelines which track continuous change, snapshots represent discrete temporal observations.

Key considerations when working with snapshots:

  • Determining appropriate snapshot frequency (daily, weekly, monthly)
  • Balancing storage costs against analytical requirements
  • Managing snapshot granularity for different use cases

This section covers patterns for working with snapshots in the presentation layer:

  • Granularity and redundancy: Strategies for managing snapshot detail levels: example.
  • Fidelity: Ensuring snapshot accuracy and consistency: example.