Suppose you have three OEM data feeds — Huawei, Enphase, and Solarman — landing in separate database tables with different column names, different timestamp formats, and different error representations. Your dashboards work, your alerts fire, but every cross-site comparison requires manual translation. You don't need a platform migration. You need an insertion layer.
ODSE is not a platform migration. It is an insertion layer between raw OEM telemetry and your downstream analytics. Keep your current data lake, dashboards, and alerting tools intact.
Where to Insert ODSE
Insert ODSE after ingestion and before aggregation or modeling. Everything upstream stays the same; everything downstream gets a consistent contract.
raw ingest -> parse -> ODSE transform -> ODSE validate -> existing warehouse/modelsWeek Plan
Day 1-2: Pick your noisiest OEM source and map it end-to-end into ODSE. Focus on timestamp normalization, energy field mapping, and error code classification. Day 3: Gate the output with schema validation — if records don't pass, they don't enter the analytics pipeline. Day 4-5: Bring in the remaining two OEMs, resisting the temptation to build per-source special cases. Day 6-7: Run a single portfolio rollup query across all three sources and set up regression checks to catch future transform drift.
Acceptance Criteria
You're done when three conditions are true: one output contract, one validation report, and one cross-OEM query path for faults and energy totals.
Common Failure Modes
The three most frequent causes of false conclusions after retrofit: per-OEM "temporary exceptions" that become permanent, unvalidated backfills that introduce duplicates, and timezone drift that shifts intervals across boundaries.
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