Workflows: moving data toward a useful outcome
Assemble source, connectivity, protocol, data, mapping, spatial context, rules, and outputs without hiding them inside one black box.
Why this matters
A workflow is easier to troubleshoot and reuse when every stage has a clear responsibility. When all layers are called 'integration,' nobody knows where a failure actually occurred.
Learn it
Start with the source or observation asset. Identify how it connects, which protocol or interface is used, and what data representation arrives. Validate and normalize only what needs normalization. Add spatial context. Evaluate rules or events. Then route the result to storage, a map, API, export, notification, collaboration layer, or approved tool. Preserve provenance across the path.
Work through an example
A field submission can arrive through a connector, map its coordinates and observation fields into a workspace dataset, test whether it falls inside a project area, and publish an event for review.
source → connectivity → protocol/interface → data → mapping/validation → spatial context → rule/event → usePractice
- 1Draw one workflow using the exact stages from this lesson.
- 2Mark which stages change the data and which only transport it.
- 3Pick one failure and identify the smallest stage you would inspect first.
Try it
Follow data through a workflow
Select each stage. The goal is to keep the source, connection, protocol, data, spatial context, rule, and use understandable instead of collapsing everything into one box.
The real-world fact, device, file, application, person, or system that starts the workflow. If it is an observation, preserve where, when, what, source, value, units, and provenance.
What to watch for
Avoid diagrams where arrows have no meaning. Label what moves between stages: bytes, messages, records, observations, events, or results.
Where this takes you
This model supports Atlas DataSynch, AtlasEvo, AtlasAgents, field workflows, open-network integrations, and similar systems outside Atlasemoji.