data-throughput-accelerator

What problem does it solve?

Use 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 verifiable proof.

Core Features & Use Cases

• 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 to skip completed files/partitions; apply partitioning and clustering that match reads and writes; batch small files and requests; ensure writes are idempotent through unique keys, manifests, or replaceable staging.
• Workflow transparency: Measure backlog, run safe catch-up benchmarks, compare variants, and promote the fastest path while maintaining manifest counts and max timestamps.
• Guardrails & accounting: Preserve raw data, avoid silent failures, and keep explicit accounting blocks to prove correctness.

Quick Start

Run the throughput accelerator on a backlog to measure backlog and initiate a safe catch-up workflow.