✅ 1️⃣ Data Lifecycle Overview Data Collection User behavior logs (clicks, views, purchases) via APIs Product catalog from internal DB Product images & descriptions from CMS Processing Batch + streaming pipelines (daily + real-time) Feature engineering: User embeddings Product embeddings Data normalization & deduplication Storage Raw data → Data lake (S3 / GCS) Processed data → Feature store Curated datasets → Versioned storage Training Usage Daily training on latest stable dataset A/B testing datasets for experimentation --- ✅ 2️⃣ Versioning Strategy Version Naming Convention dataset-{type}-{YYYYMMDD}-v{major}.{minor} Example: dataset-reco-20260319-v2.1 Versioning Type Hybrid approach: Full snapshots (weekly) Incremental updates (daily) Dataset Tagging stable → production-ready experimental → testing rollback → previous stable drifted → flagged for issues --- ✅ 3️⃣ Data Update Workflow New Data Ingestion 1. Stream data into raw storage 2. Trigger ETL pipeline 3. Append to incremental dataset Validation Process Schema validation Missing/null checks Anomaly detection Version Release Flow Raw → Processed → Validated → Version Created → Tagged → Released --- ✅ 4️⃣ Data Storage & Management Storage Architecture Data Lake: AWS S3 (raw + processed) Warehouse: BigQuery / Snowflake Feature Store: Feast Backup Strategy Daily automated backups Cross-region replication Access Control Role-based access (RBAC) Separate environments: Dev / Staging / Production --- ✅ 5️⃣ Model–Data Alignment Mapping Strategy Each model version linked to: Dataset version Feature set version Example: model-reco-v5.2 → dataset-reco-20260319-v2.1 Reproducibility Store: Dataset hash Training config Feature pipeline version --- ✅ 6️⃣ Tooling & Infrastructure Recommended Tools Versioning: DVC Pipeline: Apache Airflow / Prefect Storage: AWS S3 Experiment Tracking: MLflow Feature Store: Feast Integration Approach Git + DVC for version control CI/CD pipeline for automated dataset release Airflow orchestrates ingestion + validation --- ✅ 7️⃣ Quality Control & Validation Data Integrity Checks Schema enforcement Duplicate detection Data completeness metrics Drift Detection Monitor: User behavior changes Product distribution shifts Cleaning Workflow Outlier removal Missing value imputation Standardization --- ✅ 8️⃣ Governance & Documentation Metadata Tracking Dataset version Source Timestamp Feature definitions Change Logs v2.1 → Added new user clickstream features v2.0 → Major schema update Compliance GDPR-style anonymization PII masking Data retention policies --- ✅ 9️⃣ Implementation Roadmap Phase 1: Setup Configure S3 + DVC Define versioning schema Set up pipelines Phase 2: Automation Automate ingestion + validation CI/CD for dataset releases Phase 3: Adoption Train team on workflows Enforce version usage in training Phase 4: Scaling Add real-time pipelines Optimize storage & cost Introduce monitoring dashboards --- 🎯 Key Outcome This framework ensures: Full reproducibility of models Clear dataset lineage tracking Scalable data management for continuous updates Strong governance & quality control --- If you want, I can generate: A beginner-friendly version A startup (low-cost) version Or a large enterprise-grade architecture