Subcommands: ingest Run pipeline to move data from sources to sinks. validate Run QC checks against dataset or manifest. transform Apply transforms and produce outputs. serve Run as long-lived ingestion daemon. inspect Show dataset / manifest metadata. compact Consolidate chunked outputs into an archive. monitor Stream runtime metrics.

Global options: --config FILE Config YAML/JSON --threads N Worker threads (default 4) --chunk-size SIZE Chunk size (default 8M) --log-level LEVEL info|debug|warn|error --log-format FMT text|json --dry-run Validate config without transferring data

Global example: qcdma-tool --config /etc/qcdma/config.yaml --threads 8 --chunk-size 16M ingest --source file:///data/incoming --sink kafka://broker:9092/topicA --qc temporal-consistency

This document explains qcdma-tool v2.0.9: what it is, its purpose, major features and changes in this release, architecture and components, usage patterns and examples, typical workflows, configuration and command-line options, troubleshooting, common pitfalls, and suggestions for extending or integrating the tool. Assumptions: qcdma-tool is treated as a command-line utility for working with QC/DM A (Quantum-Classical Data Management/Acquisition) — a hypothetical but plausible domain combining high-throughput data acquisition, quality control (QC), and DMA-like direct-memory access patterns for large datasets. Where behaviour or specifics are ambiguous, realistic and practical assumptions are made to create a coherent, useful exposition.

Author

qcdma-tool v2.0.9
Stefania Vichi
Head of Growth at Noloco
qcdma-tool v2.0.9qcdma-tool v2.0.9qcdma-tool v2.0.9

Stefania leads Growth at Noloco, where she’s focused on scaling marketing, driving customer acquisition, and helping more businesses discover the power of building apps without code. With a background in SaaS growth &marketing and a sharp eye for strategy, she brings a data-informed approach to everything from SEO and content to product-led growth. On the blog, Stefania writes about go-to-market strategy, growth experiments, and how AI is reshaping the way teams market, onboard, and scale software products.

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