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Data Engineering Model
1. Sequence Model
Source > Process > Sink
This is the simplest and most common pattern.
- Data flows in a straight line
- Each step transforms the data
- Typically implemented as Bronze > Silver > Gold
Where it fits
- ETL pipelines
- Batch processing
- Data cleaning and enrichment
Example
Raw logs > cleaned logs > aggregated reports
Funnel Model
Multiple Sources > Process > Single Sink
Here, multiple inputs are combined into one destination.
- Data from different systems is merged
- Requires schema alignment and joins
- Often introduces data quality challenges
Where it fits
- Data warehouse ingestion
- Building unified datasets
- Customer 360 views
Example
CRM + Transactions + Web logs → Unified customer table
Fan-Out (Star) Model
Single Source > Process > Multiple Sinks
One dataset feeds multiple downstream consumers.
- Same data used in different ways
- Different outputs for different use cases
- Requires careful data contracts
Where it fits
- Serving layer
- Analytics + ML + APIs from same data
- Reverse ETL
Example
Gold table > BI dashboards + ML models + APIs