Hi everyone, I'm relatively new to managing our data infrastructure and I've just helped our team migrate one of our key ETL pipelines from an on-prem Radware appliance to their cloud service. The move was mostly for scalability, but I'm a bit nervous because some things didn't translate directly.
The biggest surprise was around how the cloud service handles "state" for some of our longer-running data jobs. On-prem, we had a persistent connection for the full extract phase. In the cloud, we hit timeout settings we weren't expecting, which caused a partial load to be marked as complete. We lost about two hours debugging why our fact table counts were off.
Our old configuration snippet for the job looked roughly like this:
```
job_timeout: 10800 # 3 hours
persistent_session: true
```
In the cloud portal, the equivalent setting seemed to be under a different name (`session_linger`) and had a maximum value that was lower than our job required. We had to break the job into smaller chunks.
Has anyone else run into similar issues? Specifically:
* Are timeouts and session management the biggest differences to watch for?
* Did you find any cloud-side configurations that *improved* over the on-prem appliance that we should switch to?
* How do you handle monitoring now? Our old system dumped logs to a local server; the cloud service's logging feels a bit more opaque.
I'm worried about what other "safe" patterns from on-prem might break in subtle ways. I'd really appreciate any lessons learned from those who've made this jump.
I'm an IT ops lead at a mid-sized logistics company. We've been on Radware's cloud service for about eight months after a similar on-prem move, handling daily inventory and shipment ETL batches.
Session persistence and timeouts: Exactly your issue. The cloud service's session_linger maxes at 1 hour. We had to redesign jobs longer than that to checkpoint every 45 minutes. It's the single biggest config difference.
Hidden cost in data transfer: Our on-prem box had predictable costs. Cloud pricing got tricky with inter-region data movement for disaster recovery. Our bill was 20% higher than projected in month one until we optimized that.
Management and visibility: The cloud portal's real-time metrics for connection queues and health checks are superior. We caught three latency spikes before users complained, which the old appliance logs wouldn't show us quickly.
Scalability vs. control: Scaling is a button click, but we lost fine-grained control over some network kernel parameters we used for extreme tuning. The trade-off is fixed.
I'd recommend the cloud service for new projects or if your team values agility over deep, low-level control. To decide, tell us your average job runtime and whether you need to customize TCP stack behavior.