data-throughput-accelerator

Topics in the source

• Data Throughput Accelerator
• First Distinction
• Fast Path Heuristics
• Workflow
• Accounting Output
• Guardrails

Use this skill when the bottleneck is moving, transforming, or saving lots of data. The goal is not just speed. The goal is faster correct data landing in the right place with proof. Separate these before optimizing: source extraction speed; network transfer speed; warehouse/load speed; transform speed; serving-table freshness; live tail growth while the job runs. A pipeline can be "fast" and still appear behind if new data arrives faster than the final catch-up window. Move compute to where the data already is. Prefer warehouse-native scans, joins, and appends for large landed files. Use manifests or checkpoints so completed files/partitions are skipped. Use partitioning and clustering that match the read and append pattern. Batch small files, requests, and writes. Make writes idempotent through unique keys, manifests, or replaceable staging. Keep raw, derived, and serving tables separately accountable. 1. Read the current source, target, and manifest contracts. 2. Measure backlog: external files, manifest rows, raw rows, derived rows, min/max timestamps, and unprocessed counts. 3. Run a safe catch-up or sample benchmark. 4. Compare variants: batch size, worker count, warehouse SQ

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