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prhoguns/README.md

Philips Rhoguns

I build data pipelines, security detections, and the Linux, database and network infrastructure underneath them. Based in Toronto. My background includes leading a York University campus physical security control-room shift, where I handled alarm triage, emergency escalation and operator training.

Portfolio and case studies · LinkedIn · Email

Start with these projects

Project What I built Proof
Toronto Crime SQL Analytics Twenty SQL questions, generated results and a live dashboard from City open data. Findings · Case study
Toronto Open Data Pipeline CKAN extraction → PostgreSQL → 11 dbt models → Airflow, with a prototype TTC delay model. Run results and limitations · Case study
PostgreSQL DBA Toolkit Primary, replica, WAL archive, diagnostics and repeatable recovery and failover drills. Drill output · Case study
Sigma Detection Pack Eleven detection rules tested against planted positives and near-misses. Tests · Case study

The project READMEs include setup steps, design decisions and limitations. These are independent portfolio projects and lab builds; their results are reported with the test conditions that produced them.

More by area

CompTIA Security+ · AWS Certified DevOps Engineer – Professional · Microsoft Power Platform Developer Associate

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  1. toronto-crime-sql-analytics toronto-crime-sql-analytics Public

    20 business questions answered in SQL on a decade of Toronto police data (DuckDB, window functions, CTEs), with findings and charts

    Python

  2. toronto-open-data-pipeline toronto-open-data-pipeline Public

    ELT pipeline for Toronto open data: CKAN → PostgreSQL → dbt star schema → Airflow, with Power BI marts

    Python

  3. postgres-dba-toolkit postgres-dba-toolkit Public

    Working PostgreSQL primary/replica lab with verified point-in-time recovery and failover drills, a diagnostics query library, measured index tuning and DBA runbooks

    Shell

  4. sigma-detection-pack sigma-detection-pack Public

    Detection-as-code: Sigma rules converted to SQL, Splunk and Sentinel KQL with pySigma, tested in CI against a corpus with planted true positives and near-misses

    Python