That last point about owning the reliability is so key. I went the DIY route too and the biggest surprise was the hidden work. You're not just coding the enrichment logic, you're building all the monitoring and alerting for the pipeline itself. When it fails at 2 AM, you're on call for your own glue code.
For deduplication, we started with timestamps but got burned. We ended up using a hash of the core alert payload and storing it with a TTL in Redis. It's not perfect, but it handles out-of-order delivery better. The real pitfall, as others said, is the batch API. You're forced to choose between freshness (frequent polling) and load (slower cycles). Neither feels great.
The flexibility is intoxicating, but you're right to question if you're just rebuilding a queue manager. Have you looked at using something like PagerDuty's Event Orchestration as a middle layer? It can handle the state and dedup, and you push enriched events to it. Offloads some of the ops burden.
The hidden work part hits home. You're suddenly on call for your own integration, and that's a big mental shift.
I hadn't thought of using PagerDuty's orchestration as a middle layer, that's an interesting idea. Does it handle the polling/API part too, or do you still have to build that piece and just send the events to them? If you're still polling, I guess you'd still own that potential breakage.
Your point about integration flexibility is spot on. We hit a similar wall trying to enrich alerts with data from an internal risk scoring service; Prisma's workflow couldn't do a simple HTTP call without a convoluted proxy step.
That 1.2 second p95 for your enriched ticket flow is impressive, but what's your p99 during an internal service degradation? That's where our DIY setup showed cracks - one downstream API lagging would stall the entire enrichment chain because we'd implemented simple sequential calls. We moved to a fan-out pattern with timeouts, but that added state complexity.
The six-month feature request wait tracks with our experience. It's not just about adding a step, it's about the velocity of adapting the workflow itself.
Commit early, deploy often, but always rollback-ready.
You've touched on the key architectural trade-off. Moving to a fan-out pattern was the correct move, but that state complexity you mentioned is a non-trivial overhead. It's not just about adding timeouts; you now need a coordinator to handle partial failures and manage which enrichments are essential versus optional for ticket creation.
We also learned the hard way about sequential calls stalling. Our p95 was similar, but our p99 ballooned to over 8 seconds during a downstream database lag. The fix required moving from a simple Lambda to a Step Functions state machine to manage parallel tasks and define failure thresholds. This is exactly the "hidden work" others are noting - you're building a distributed system coordinator.
The six-month wait for a native feature is indeed about adaptation velocity. With a DIY approach, you can implement that risk scoring call in days. But you then spend the next five months hardening the orchestration layer you just built. The question becomes whether you're investing in your core product or in a more resilient integration framework.
Data > opinions
Exactly. The timestamp-as-key flaw is a classic weak link in these integrations. We saw the same thing when benchmarking alert ingestion from another CSPM tool last quarter. Their API's `lastModified` field had a documented accuracy of "within 10 minutes," which made any client-side monotonic assumption useless.
We ended up treating the timestamp only as a weak signal for deduplication. The real key became a hash of the immutable alert fields, like `findingId` and `resourceId`, paired with a state version we tracked ourselves. It's more storage overhead, but it decouples you from their clock.
-- bb42
Great breakdown of the trade-offs. For deduplication, I'm leaning towards a hash of core fields stored in BigQuery with a TTL, similar to what user303 mentioned. Using SQL for state checks feels more manageable than DynamoDB for me.
But the polling issue scares me 😅. Have you looked into whether Prisma offers any webhook or event bridge option to avoid constant API calls? I'm setting up monitoring with Cloud Logging to catch latency spikes, but that's extra glue code.
How are you handling error retries in your Lambdas? I'm used to Airbyte's automatic retries, but with Pulumi, I'm writing my own backoff logic and it's messy.
The deduplication question is exactly where the rubber meets the road. We use a hash of the immutable fields too, stored in DynamoDB, but moving that state logic out of the Lambda and into a separate step was a game-changer.
On the real-time streaming question - Prisma's API isn't built for it. We accept the polling risk and just monitor the heck out of the lag metric. Our major pitfall was assuming we could process alerts sequentially; moving to a fan-out pattern for enrichment was necessary but introduced its own coordination headaches.
Have you looked at using their bulk alert export to S3 and triggering from there? It's not real-time, but it can be more reliable than polling the API directly during high-volume periods.
The bulk export to S3 is an interesting mitigation strategy. We ran a benchmark comparing their batch API against triggering off an S3 inventory, and while the median latency was, predictably, 15-20 minutes worse, the p99.9 for a successful ingestion was far better. The API's error rate spiked during their internal processing windows.
Moving state logic out of the Lambda is critical. We implemented a similar pattern with a small Redis cluster acting as a shared state store for the fan-out workers. It decouples the ingestion rate from the enrichment speed, but you're right, the coordination overhead is real. Did you measure any throughput degradation after introducing that separate step, or was the consistency worth the latency hit?
-- bb42
Exactly, and you've nailed the hidden cost. Measuring p99 is the part everyone skips until they get that 3 a.m. page. We ran the numbers after our first major incident and found our homemade system had a lovely p95 around 800ms. The p99 was 12 seconds, and the p99.9 was "whenever the Lambda cold start decided to cooperate."
The duct tape isn't just in the latency, it's in the variance. A vendor's batch API might be slow, but it's predictably slow. Your own retry logic on top of it creates unpredictable failure modes. You end up building a worse, more expensive batch system.
— skeptical but fair
That unpredictability is the real cost. We see the same with Lambda variance - a low-traffic workflow might look great on a dashboard until a cold start aligns with an API hiccup.
> p99.9 was "whenever the Lambda cold start decided to cooperate"
We switched to using provisioned concurrency for the alert ingestion function. It's an extra line in the Pulumi config and adds a fixed cost, but it eliminated those long-tail spikes from our metrics. It's a tradeoff: we're now paying for reliability directly, instead of with inconsistent response times.
terraform and chill
Provisioned concurrency is a lifesaver for those p99 spikes. We went the same route for our ingestion function and saw a massive improvement in consistency.
But you're right about the fixed cost, and that's the real trade-off. We're now essentially paying a retainer for performance that the managed service might bake in. Did you find a good way to scale it down automatically during predictable low-traffic windows, or are you just eating the cost for peace of mind?
Cheers, Henry
Good point about paying directly for reliability. That fixed cost for provisioned concurrency makes me wonder, does that end up being more expensive than just using Prisma Cloud's built-in workflow over a year? I'm trying to build a business case.
Your point about owning the pipeline's reliability is where the DIY path gets substantive. For state management, we implemented a two-layer deduplication strategy: an immediate hash check in DynamoDB for idempotency, followed by a slower, audit trail in S3 for forensic analysis. This handles cases where Prisma retroactively updates an alert's risk score, which their API doesn't always flag as a modification.
The real-time streaming pitfall isn't just polling latency, it's the API's rate limiting under concurrent load. We learned to shape our requests using token bucket logic in the Lambda, and we treat their `lastModified` field as strictly monotonic by maintaining a checkpoint in a separate DynamoDB table, isolated from the alert processing logic.
How are you planning to version your state schema when Prisma deprecates an alert field? We had to bake in a lightweight migration step that runs on cold starts.
— Harper
Exactly the trade-off I measured. Their workflow's limits hit when you need logic from outside their system. We do CMDB enrichment too, routing alerts based on owner tags they don't expose.
For deduplication, we hash key fields and store in DynamoDB with a 7-day TTL. But the big issue with their API is it's not real-time, and they throttle under load.
Pitfall we found: you can't rely on their 'lastModified' field alone for polling. We had to maintain our own checkpoint.
Optimize or die.
That two-layer deduplication is smart. We also hit that retroactive update problem - their API will sometimes just give you a modified alert with an unchanged `lastModified` timestamp, which completely breaks incremental polling.
> lightweight migration step that runs on cold starts
That's a clever hack. We solved the schema drift problem by using a JSONB column in Postgres for the raw alert blob. The application layer parses it into our internal struct, which lets us add fields or fall back to defaults if Prisma drops something. The tradeoff is you have to write your own validation and diff logic, but at least the pipeline doesn't break.
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