🎛️ Flow Orchestration Overview
How flows, rules, decision trees, collective triggers and trigger schedules fit together to orchestrate data processing in the platform.
🗺️ Flow Design
Design configurable message processing pipelines using flow sequences and rules to filter, validate, and transform ingested data.
📐 Rules
Create Python-based rules that process timeseries data, validate measurements, and perform transformations within flows and configurations.
🎛️ Rule configurations
Configure custom rules with parameters and widgets to validate, estimate, calculate, or forecast timeseries data in flows.
🧑💻 Custom Rules
9 items
🔌 Custom Market Adapters
1 item
🤖 Machine Learning
Upload and deploy XGBoost or SKLearn machine learning models, then use them in rules to generate predictions on timeseries data.
🌳 Decision Trees
Implement conditional flow branching logic that dynamically determines which flow to chain based on current flow properties.
🚨 Anomaly Detection and Alerting
The end-to-end path from detecting a data anomaly in a rule to automated action: annotations, tasks, corrective flows, and notifications.
🤝 Collective Trigger
Collect messages from multiple flows and trigger a new flow when specified conditions are met using correlation IDs and configurable timeouts.