data-throughput-accelerator

Name: data-throughput-accelerator
Author: affaan-m

Accelerate large-scale data pipelines, backfills, and syncs without sacrificing correctness.

Stars

★ 229,796

Source

GitHub

Updated

2026-07-15

// securityLow risk

Prompt only, no code
Open source
Community-trusted· 229.8k

Running security audit…

Credentials
Network egress
Code execution
Data access
Supply chain

// install

Copy the install command and let the AI configure it · recommended for beginners

Please install the "data-throughput-accelerator" skill from askskill:
1. Download https://raw.githubusercontent.com/affaan-m/ECC/main/skills/data-throughput-accelerator/SKILL.md
2. Save it as ~/.claude/skills/data-throughput-accelerator/SKILL.md
3. Reload skills and tell me it's ready

// download

Download SKILL.mdInstall manifest ↗

// examples

Speed Up Historical Backfills

Prompt

Design an acceleration plan for a data warehouse backfill covering 5 billion records. Reduce runtime while preserving idempotency, integrity validation, and failure recovery. Include sharding strategy, concurrency control, retry logic, validation steps, and monitoring metrics.

Expected

An actionable backfill acceleration plan with batching, parallelization, correctness safeguards, and monitoring recommendations.

Optimize ETL Ingestion Performance

Prompt

Analyze performance bottlenecks in the current ETL ingestion flow and propose improvements to speed up loading large CSV batches into a data warehouse. Focus on bulk writes, compression, partitioning, parallel processing, and deduplication validation.

Expected

A set of ETL optimization recommendations showing how to increase throughput while maintaining data quality.

Improve Table Synchronization Efficiency

Prompt

Create a speedup plan for cross-database table synchronization. The source table receives 200 million new rows daily, and incremental sync must finish faster without missing, duplicating, or reordering data. Provide the sync architecture, checkpointing, recovery, and consistency validation approach.

Expected

A high-throughput table sync plan covering incremental design, fault tolerance, and consistency verification.

// docs

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

Read the current source, target, and manifest contracts.
Measure backlog: external files, manifest rows, raw rows, derived rows, min/max timestamps, and unprocessed counts.
Run a safe catch-up or sample benchmark.
Compare variants: batch size, worker count, warehouse SQL, file grouping, staging shape, and manifest update method.
Promote only the fastest path that keeps counts and timestamps coherent.
Codify the path as a CLI, scheduled job, workflow, or runbook.
Rerun final accounting after the codified path executes.

Accounting Output

Use a hard accounting block:

Data throughput result:
- Source files discovered: 294
- Files processed this run: 294
- Raw rows added: 9,683,598
- Derived rows added: 8,917,585
- Remaining tail: 24 files at readback time
- Runtime: 38.7s
- Correctness gate: manifest counts and table max timestamps match

Guardrails

Do not delete raw data to make a metric look better.
Do not skip failed files silently.
Do not mix historical backfill status with live-tail freshness.
Do not call a pipeline complete until the target tables and manifest agree.
For finance, healthcare, regulated, or customer-impacting data, preserve replay evidence and approval gates.