```json { "table_name": "pipeline_stage_status", "primary_key": "id", "candidates": [ { "rank": 0, "partition_key": "batch_id", "partition_key_label": "E", "partition_key_reasoning": "batch_id is the primary entity organizing this pipeline execution log. RPUV=6.95 indicates ~7 stage records per batch (download, bronze, classify, etc.), which is ideal for grouping related pipeline stages. Top 3 coverage of 3.06% shows good distribution. Semantically, each batch represents a complete pipeline run through multiple stages.", "clustering_key": "started_at", "clustering_key_label": "S", "clustering_key_reasoning": "started_at orders the pipeline stages chronologically within each batch. The data shows stages execute sequentially (download→bronze→classify→tags→analytics→search→catalog→gold→sample_queries→dashboards→reconcile). Uniqueness=1.000 confirms each stage start is distinct, enabling proper temporal ordering of the pipeline workflow.", "world_line": "batch_id", "session_column": null, "confidence": "HIGH", "notes": "This is the primary perspective: tracking a batch's journey through the pipeline stages. Each batch_id groups 7-11 stage executions in temporal order, making it the natural partition for analyzing pipeline performance, stage durations, and failure patterns." }, { "rank": 1, "partition_key": "dataset_id", "partition_key_label": "E", "partition_key_reasoning": "dataset_id is a valid alternative entity perspective. RPUV=12.83 indicates ~13 stage records per dataset across all batches. Top 3 coverage of 23.75% shows d_9e733a56 has 205 records (multiple batches over time). This perspective tracks a dataset's processing history across multiple ingestion attempts.", "clustering_key": "started_at", "clustering_key_label": "S", "clustering_key_reasoning": "started_at provides chronological ordering across all batches for a given dataset, enabling analysis of dataset reprocessing patterns, historical pipeline runs, and evolution of processing times over multiple ingestion cycles.", "world_line": "dataset_id", "session_column": "batch_id", "confidence": "MEDIUM", "notes": "Alternative perspective for dataset-centric analysis: tracking all pipeline runs for a specific dataset over time. Useful for understanding dataset-specific processing patterns, but less natural than batch-level grouping since pipeline stages are fundamentally organized by batch execution." } ] } ```