Skills · SkillData & AIReviewed

data-throughput-accelerator

Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking variants, and codifying the fastest path with a hard accounting block proving rows and timestamps cohere.

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Kind: Skill
Works with: Claude Code, Codex
Source: affaan-m/ECC @ d3b8a3e90890
Path: skills/data-throughput-accelerator/SKILL.md
License: MIT
Jobs: Debugging, Guardrails, Automation

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# Data Throughput Accelerator

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.

## First Distinction

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.

## Fast Path Heuristics

- 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.

## Workflow

1. Read the current source, target, and manifest contracts.
2. Measure backlog: external files, manifest rows, raw rows, derived rows,
…