The spreadsheets are pointing at the right symptom, but they miss the underlying infection. You're not building a normalization layer, you're building a *schema*. That's the strategic asset.
Every legacy log you normalize isn't just data you can cap or filter, it's a data product with a contract. Once you define that contract - the fields, the types, the SLO for freshness - the specific forwarder or plugin is just an implementation detail you can swap out. The cost center becomes a reusable blueprint.
The trick is convincing the finance team that you're building a factory floor for security data, not just patching another leaky pipe. Good luck with that.
You're right about the buffering, but that's just the start. Their rate limits are documented, but their queue behavior under load isn't. I've seen their HTTP 429s come with inconsistent retry-after headers during sustained spikes, which breaks naive exponential backoff.
Your forwarder needs to monitor its own queue depth and shed non-essential log types before it hits the limit, otherwise you're just building a buffer that fails catastrophically. Did your tests include a sustained surge over their stated limits?
-- bb
Ah, that API snippet brings back memories of building our custom syslog-to-HTTP bridge for an old AS/400. The REST API is indeed clean for structured logs, but you've hit the exact pain point with legacy systems - they rarely output clean JSON.
When you're dealing with those oddball TCP streams and non-standard file logs, you'll likely need a parsing layer *before* the Vision One API. I've had good results using a lightweight log forwarder like Fluent Bit or Vector on a jump box near the legacy systems. They can handle the raw TCP/file ingestion, apply some basic parsing/transformation, and then forward via the Vision One HTTP endpoint.
One gotcha: their timestamp parsing. If your legacy app logs timestamps in some weird format like "DD-MMM-YYYY HH:MM.SS", you'll need to normalize that to ISO 8601 before sending. The API isn't forgiving about timestamp formats. Learned that the hard way when our mainframe logs showed up with incorrect event times because of a daylight savings quirk in our parser.
Also, watch the batch sizing in your script. Their API has limits on maximum payload size, but sending thousands of tiny requests will hit rate limits fast. We found batching to 100-200 events per call was the sweet spot.
Integration Ian