Automate with lavaflow
A lavaflow connects data sources, processing steps and targets on a shared canvas.
From draft to run
Choose a source
Open lavaflow and create a workflow. Add a landing zone, table, connection or HTTP query as a source. A landing area must already be configured in the installation.
Connect steps
Add column mapping, Python, JavaScript, SQL or quality checks. Conditions and switch nodes route rows; merge nodes append compatible data streams.
Set a target
Choose a table or file output. For tables, check whether the run creates, appends, replaces or reconciles by key.
Test and publish
Save and check the draft with a test run. Publish the reviewed version before running it or scheduling it.
Two ways to use Pyro
| Building block | Behavior |
|---|---|
| Pyro transformation | Pyro proposes Python. After review and acceptance, the code is saved and runs again without another AI call. Input schema changes require the code to be reviewed again. |
| Pyro step | Classifies, extracts or summarizes using a model on each run. Requires a local model or API access; personal subscription sign-ins cannot be used here. |
Trigger and inspect
Manual, scheduled or new-file triggers start the published version. Run history shows individual step results. Platform actions can start jobs, call subflows, refresh dashboards or generate reports.
Check multiple targets
Multiple targets do not form a single transaction. A successfully written target remains changed if another target fails. Check target states before repeating a run. Test runs do not write targets; HTTP send actions use a dry run and require confirmation before their first real delivery.
Applies to: This page describes lavalake 0.201.0. Product and documentation live in separate repositories — when in doubt, what the console shows is authoritative.
Something missing?
If these pages do not answer your question, we will walk through the platform against your own case in half an hour.
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