Key Takeaway
Data pipeline design docs for AI should treat data quality validation as a first-class pipeline stage, not an afterthought, because data quality directly determines model quality.
When to Use This Template
Use this design doc when building data pipelines that feed ML training, inference, or analytics systems. This template is specifically designed for AI data pipelines, which have stricter quality requirements, lineage tracking needs, and schema stability concerns than general ETL pipelines. It covers ingestion, transformation, validation, feature store integration, and operational procedures.
Template Sections
Document every data source that feeds the pipeline: Source name and description, Access method (API, database query, file drop, stream), Data owner (team or individual responsible), Freshness (how often source data is updated), Volume (current and projected data volume), Schema (field definitions, types, constraints), and SLA (availability guarantees from the source team). This catalog becomes the primary reference for understanding data dependencies and diagnosing pipeline issues.
Document the ingestion approach for each source: Processing mode (batch with schedule, streaming with consumer group, hybrid), Schema evolution handling (how to handle source schema changes without breaking the pipeline), Error recovery (retry strategy, dead letter queue, manual intervention procedures), Idempotency (how to handle duplicate ingestion safely), and Backfill procedure (how to re-ingest historical data when needed).
Document transformation logic and quality gates: Transformation steps (ordered list of transformations with input/output schemas), Idempotency guarantees (every transformation must produce the same output given the same input), Quality validation rules (null checks, range validation, referential integrity, distribution checks), Failure handling (which quality failures block the pipeline vs. generate alerts), and Testing approach (unit tests for transformations, integration tests with sample data).
Document feature store integration and lineage tracking: Feature definitions (name, type, computation logic, serving latency), Online vs. offline serving (which features need real-time serving), Backfill strategy (how to compute features for historical data), Lineage tracking (tool selection, what metadata is captured at each stage), and Impact analysis (how to determine which models are affected by a data source change).
Document operational procedures: Monitoring (pipeline health metrics, data quality dashboards, freshness tracking), Alerting (alert definitions with thresholds, on-call routing, escalation path), Maintenance (schema migration procedures, dependency updates, performance optimization), Disaster recovery (backup strategy, recovery time objective, recovery point objective), and Cost management (compute and storage cost tracking, optimization opportunities).
data-pipeline-design-doc.md
MD · 9 KB
Complete data pipeline design document template in Markdown format
The most common data pipeline failure mode is a source schema change that silently corrupts downstream features. Implement schema validation at ingestion as a blocking gate, and set up notifications when source teams announce schema changes.
Version History
1.0.0 · 2026-03-01
- • Initial data pipeline design document template