dbt-materializations

Dbt materializations determine how your model is stored and updated. Choose based on query patterns, update frequency, and data size: ephemeral for intermediate steps, views for small datasets, tables for large fact tables, incremental for frequent updates, and snapshots for historical tracking.

Incremental materializations rebuild only new or changed rows, drastically reducing build time for large tables. Use them when you have millions of rows and frequent updates—build time drops from hours to minutes while maintaining accuracy.

Snapshots capture row-level changes over time using SCD Type 2 logic. Configure dbt snapshots with unique keys and updated-at timestamps to automatically track when customer or dimension attributes change, enabling historical analysis.

Views recompute on every query, saving storage but increasing query time; tables precompute and store results, using more storage but delivering faster queries. Choose tables for heavy query patterns and views for rarely-accessed intermediate data.

Yes, dbt supports Python models across materializations including ephemeral, table, and incremental. Use Python models in data pipelines when you need machine learning libraries or custom transformations alongside SQL-based materializations.

Incremental models support strategies like merge and delete+insert, plus clustering configurations for Snowflake. Set incremental_strategy, unique_key, and cluster_by to optimize performance and storage for your update patterns.