Data pipeline architecture
Data discovery catalog
Index dataset ownership and metadata without copying every data record.
Architecture example library
100 original problem patterns. 10,000 configurable reference variants. Explore a design, download its source, or create a private copy to customize.
Variants combine a problem pattern, deployment context and design concern. They are generated reference compositions, not independently audited production systems. Original library source is MIT licensed.
1,000 public template files
Data pipeline architecture
Index dataset ownership and metadata without copying every data record.
Data pipeline architecture
Version training inputs and model evaluation before promotion.
Data pipeline architecture
Separate online prediction, feature reads and asynchronous monitoring.
Data pipeline architecture
Expose retrieval and model context boundaries rather than hiding them inside one AI box.
Data pipeline architecture
Track document versions and index updates through an asynchronous workflow.
Data pipeline architecture
Track deletion requests and dependent data stores with an explicit completion record.
Data pipeline architecture
Keep source extraction, validation and reporting data separate.
Data pipeline architecture
Handle continuous events with visible replay and failure handling.
Data pipeline architecture
Replicate database changes without treating the analytics copy as the transaction authority.
Data pipeline architecture
Route invalid datasets to review before making them available to consumers.
Data pipeline architecture
Index dataset ownership and metadata without copying every data record.
Data pipeline architecture
Version training inputs and model evaluation before promotion.
Original Drawarc examples include editable Mermaid and project files. For third-party GitHub source, review its upstream license before reuse. Importing never publishes someone else’s source into this gallery.
Source library & repository instructions