A landmark day for platform observability and data pipeline reliability. We shipped a fully wired Jobs monitoring page that surfaces all 13+ tracked job types, broke through a major ETL performance ceiling on the SynPUF dataset (17 hours → 14 minutes for drug_era builds), and closed out a cohort generation audit that uncovered eight discrete bugs across the SQL builders, API layer, and frontend.
A massive day on the ingestion front — 87 commits landed in Parthenon today, almost entirely focused on building out a brand-new end-to-end data ingestion pipeline. We now have a fully wired system for creating ingestion projects, uploading raw files, staging them into a schema-isolated PostgreSQL environment, and handing off to Aqueduct for ETL. This has been a long time coming.
Today was one of those satisfying days where two major workstreams converged: we pushed the Ares data quality module from skeleton to a fully featured analytics suite with four distinct intelligence phases, and we permanently fixed a cluster of compounding bugs that had been making Achilles characterization runs fragile on large real-world datasets. Both efforts move Parthenon meaningfully closer to being a production-grade OHDSI research platform.
One of OHDSI's greatest strengths is database agnosticism. The HADES ecosystem — via SqlRender and DatabaseConnector — lets researchers write analyses once and run them against SQL Server, PostgreSQL, Oracle, Snowflake, BigQuery, and seven other platforms without modification. Today, Parthenon achieved full parity with that capability: all 12 HADES-supported database dialects are now covered across both the PHP SQL translator and the R runtime.
After months of a perpetually red CI pipeline, today marks a turning point for Parthenon: 92 commits, a full-spectrum codebase review, a complete AtlanticHealth patient synthesis pipeline, and — most satisfying of all — every CI job green. Here's how we got there.
A big architectural day on Parthenon: we shipped the new Workbench launcher experience and drafted the formal plan to collapse our multi-database mess into a single, schema-isolated parthenon database. A noticeably cleaner codebase on the other side.
Today marked a significant architectural milestone for Abby, Parthenon's AI research assistant: the completion of Phase 1 of the Abby 2.0 memory overhaul. Eighty-six commits landed today, all focused on one goal — giving Abby a durable, queryable memory backed by PostgreSQL rather than ChromaDB, and surfacing user research context directly in the chat interface.
A big day focused on getting the Ask-Abby AI assistant into a genuinely reliable state — squashing a cascade of cold-start failures, wiring up a comprehensive integration test suite, and cleaning up some fixture hygiene issues that were quietly polluting our design exports. Eighty-nine commits landed in Parthenon today, and the platform feels meaningfully more stable for it.
Abby can now answer "What concept sets do we have for diabetes?" and "How many patients are in our CDM?" with real data — queried live from the Parthenon PostgreSQL database at response time. Eight contextual tools give her awareness of concept sets, cohort definitions, vocabulary concepts, Achilles characterization stats, data quality results, cohort generation counts, CDM summaries, and analysis executions.
After losing app data to an accidental Docker volume wipe and spending 24 hours restoring it, we hardened the database architecture to eliminate this class of failure entirely. The Docker PostgreSQL container is no longer the source of truth for anything — the host PostgreSQL instance owns all persistent data, and automated backups run every 6 hours.