After reviewing a surprising number of Databricks discussions around SCD2, CDC, historical reporting and temporal joins, I noticed that most historical data modeling challenges seem to fall into a small set of recurring patterns:

• Historical Backfill
• Late Arriving Dimension
• Early Arriving Fact
• Snapshot Reproducibility
• Historical Match Ambiguity
• Historical State Consolidation

What's interesting is that the implementation details differ, but the underlying modeling problems often look very similar.

Am I missing any major historical modeling patterns?

Curious how others would categorize these problems.