rill-model

To build incremental ETL data models with SQL partitioning, define the required type, sql, and materialize properties, then configure optional partitions and incremental sections for robust production-grade pipelines.

Rill data models are used for ETL and transformation tasks to define SQL logic that outputs derived tabular data to OLAP connectors, supporting structured pipelines and reproducible analytics workflows.

Use partitioned or incremental models for data transformations when building near-root DAG models that reference other models and connectors, requiring specific refresh configurations to support robust production-grade pipelines.

The best way to structure near-root DAG models is to define type: model with required sql and materialize properties, referencing source connectors and other models to produce tabular data for analytics dashboards.

Yes, you need to specify materialize as a required property when deploying Rill data models, alongside type: model and sql, to properly output derived tabular data to OLAP connectors.