Transformations
Turn source data into reusable datasets with SQL, Python, notebooks, grouping rules, or supported models, then validate the output.
Last reviewed July 13, 2026
Transformations turn raw source data into reusable datasets and activation-ready outputs. In the app, open Data, expand Integrations, then choose Transformations.
Vendo supports multiple transformation approaches in one unified layer:
| Type | Best for |
|---|---|
| SQL Transformations | Views, aggregations, joins, and custom metrics |
| Python Transformations | Advanced logic, ML preprocessing, and statistical analysis |
| Grouping Rules | Rule-based segmentation and categorization |
| Notebooks | Interactive exploration with browser-side Python |
| BQML Models | Predictive ML trained inside BigQuery |
| Attribution Models | Multi-touch attribution |
| Segments | Audience lists for ad platforms and messaging tools |
All transformation outputs can attach to output targets: user, event, session, or account properties.
Create a transformation
- Open Data → Integrations → Transformations.
- Click New Transformation.
- Choose an approach (SQL, Python, Grouping, ML category).
- Configure inputs, logic, and output target.
- Schedule or run on demand.
You can also ask the Agent to create a transformation from conversation.
Calculated properties
Calculated properties can be produced in two ways:
- Rule-based — SQL CASE expressions evaluated at query time
- Precomputed — scheduled materialization into user, event, session, or account properties
Both types appear in the Data Catalog alongside source properties. See Customer 360 for the built-in property templates.
Pipeline integration
Transformations connect to the pipeline DAG. When an upstream source sync completes, downstream transformations can trigger automatically — enabling chains like:
Source Import --> Property Compute --> BQML Retrain --> Destination SyncRelated
- Output Targets — attach outputs to catalog properties
- Customer 360 — identity and calculated properties
- Agent — build transformations from conversation
- Pipelines — operational graph