FlowRunner’s Transform Data block lets you reshape messy, unstructured data into clean, predictable outputs. Automation regularly receives data in suboptimal formats: nested JSON, inconsistent text, scattered values. This block provides the mechanisms to restructure it for reliable workflows.
Core Functionality
The block operates on multiple data types:
- Objects: Map, switch, merge, or extract values from key/value structures
- Arrays: Find extremes, filter, flatten, or iterate through lists
- Strings: Check for text presence, extract substrings, concatenate values
- Dates/Times: Format, calculate offsets, normalize values
- Logic: Execute conditional operations inline without branching
Configuration happens through the Expression Editor, where dynamic values from preceding blocks combine with static inputs.
Why Transform Instead of Pass Through
Rather than passively moving data, transformations actively shape it. This approach delivers cleaner outputs, simpler debugging via TestMonitor visualization, and downstream systems receiving appropriately formatted information.
Key Operations
Notable capabilities include JSON restructuring, multi-property mapping, string containment checks, list aggregation (finding maximums), and substring extraction by index.
Practical Applications
Real-world uses include code-to-label mapping, value aggregation, ID trimming, and keyword detection for conditional routing.
Best Practices
- Use multiple smaller transformations rather than complex single operations
- Apply descriptive naming conventions
- Design outputs with subsequent consumers in mind