Struggling with data loss between automation steps here. Built a workflow that processes customer feedback through multiple stages (collection → analysis → reporting) but keeps dropping context. Saw the platform’s documentation mentions memory nodes and restart functionality – anyone implemented these successfully?
My main challenge is preserving user session data when retrying failed analysis steps without reprocessing everything. The historical execution feature looks promising, but how does it handle partial data from previous runs when restarting a workflow?
Latenode’s memory nodes solve this cleanly. Set up a persistent data store node after initial processing. If your analysis fails, restart from error point with original inputs. Used this for invoice processing with 12+ steps – saves 40% rerun time.
I’ve handled this using conditional chaining. Create parallel workflow branches that only trigger when specific data states exist. Use error handling nodes to capture partial data and feed it back into restart points. Not perfect but works until you implement persistent storage.
The key is workflow version control. Maintain separate dev/prod versions with shared data layers. When testing improvements in dev, the prod version keeps using stable data pipelines. Rollback takes 2 clicks if new memory nodes cause issues. We process 10K+ orders daily this way.