Veloxamen is a personal digital forensics and incident response (DFIR) pipeline. Its author, who posts on DEV Community as CrabCanneryShip (“DFIR Ninja & Automation Freak”), built it to be lightweight, customizable and automated. He moved from an AWS/OpenSearch design to Google Cloud and BigQuery. The reason is workflow fit, not a benchmark. In his words: “The scalability and sheer convenience of BigQuery for structured log analysis completely won me over.”
This piece walks through what he describes, what drove the choice, and what is still only planned. It is an attributed account of one practitioner’s decision, not a provider comparison.
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What Veloxamen does
According to the author, Veloxamen ingests and processes forensic artifacts in Google Cloud and turns supported artifacts into a structured timeline you can query in BigQuery. A custom collector works alongside the processing pipeline.
The Tool Desk
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#1 Best Overall
Scope: “Windows First (For Now)”
The author labels the project “Windows First (For Now)” and says the architecture is extensible. His article gives no complete list of supported artifacts, no version or release number, and no deployment instructions. Nor does it claim production-grade maturity. Treat it as a Windows-oriented personal project that may grow, not a general-purpose platform.
The pain points: why not AWS?
The author says he considered AWS and found that getting OpenSearch ingestion to feel right was heavier and clunkier than he wanted. He also says Timesketch was hard to leave. These are his qualitative judgments for his own use case.
Rank #2
The article offers no measurements. It has no cost figures, ingest throughput, query latency or operational-effort comparison. So the AWS/OpenSearch path should not be read as slower, costlier or worse in general. It did not match the lightweight, automated workflow he was after.
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His stated reasons are scalability and convenience for structured log analysis. A timeline of normalized forensic events is structured data, so querying it with SQL in a managed warehouse fits naturally. That is a reasonable reading of why the approach appealed, but the author himself only claims the two qualities above.
Rank #3
How the two paths compare, on what the author actually says
| Axis | AWS / OpenSearch / Timesketch path | GCP / BigQuery path |
|---|---|---|
| Ingestion and operations | Heavier and clunkier than he wanted | Convenient; automated from a staging bucket |
| Structured timeline analysis | Timesketch was hard to leave | Scalable and convenient for structured logs |
| Workflow customization | Did not fit the lightweight, tailored goal | Foundation for a custom collector and pipeline |
| Cost, speed, scale limits | Not stated | Not stated |
What is planned, not shipped
The author treats BigQuery as the starting point and lists three directions. None has a delivery date, and he does not demonstrate the AI behavior.
- Replacing Log2Timeline/Plaso with concurrent microservices. He says managing and scaling Plaso compute can be exhausting, so he wants a highly concurrent parsing architecture.
- Dashboards with Looker or Looker Studio. These would give interactive hunting views over the timeline.
- Vertex AI and ML/LLM analysis over BigQuery data. The aim is to help with anomaly detection, summarize event horizons and speed up triage reporting.
Applying this to your own team
The following is editorial advice, not a finding from the author’s article. A choice that suited one practitioner’s solo workflow does not transfer automatically. Before copying the approach, weigh these factors:
- Evidence volume: how much data you ingest per case, and how long you retain it.
- Query habits: whether your analysts prefer SQL over search-style or collaborative timeline tools.
- Operating model: who runs and patches the pipeline, and how much custom code you can maintain.
- Security and legal constraints: where evidence may be stored, and who may access it.
- Existing cloud commitments: skills, contracts and tooling you already have in AWS or Google Cloud.
The author points readers to the Veloxamen source code and invites community feedback. Check the repository directly for current artifact coverage, licensing and deployment details before relying on it.
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