{"id":59220,"date":"2026-07-21T08:32:20","date_gmt":"2026-07-21T08:32:20","guid":{"rendered":"https:\/\/www.bravent.net\/news\/"},"modified":"2026-07-21T08:32:20","modified_gmt":"2026-07-21T08:32:20","slug":"dbt-snowflake-why-it-makes-sense-to-implement-them-together","status":"publish","type":"post","link":"https:\/\/www.bravent.net\/en\/news\/dbt-snowflake-why-it-makes-sense-to-implement-them-together","title":{"rendered":"DBT + Snowflake: Why It Makes Sense to Implement Them Together"},"content":{"rendered":"

There comes a point where standalone SQL is no longer enough<\/strong>. Not because it fails technically, but because nobody really knows what depends on what, who wrote a particular transformation, or whether the resulting numbers can actually be trusted.<\/p>\n

When you reach that point, you need something that brings order<\/strong> without having to replace everything that already works.<\/p>\n

dbt<\/strong><\/a> on top of Snowflake<\/strong><\/a> is a sensible answer to that problem. Snowflake provides the compute layer; dbt provides the structure. What you gain is not magic, but the ability to treat transformations as code<\/strong>: version-controlled, testable, documented, and with explicit dependencies<\/strong>. The same principles software engineering has applied for years, now brought into a space that has historically lived in a grey area.<\/p>\n<\/div>[vc_empty_space]<\/div><\/div><\/div><\/section>

The Natural Division of Responsibilities<\/strong><\/h2>\n

Snowflake does one thing extremely well: it separates storage and compute<\/strong>, scales warehouses on demand, and removes infrastructure management from the equation. You don’t have to think about indexes, physical partitioning, or how many nodes you need; warehouses scale independently and you pay only for what you use.<\/p>\n

dbt does another thing extremely well: it turns SQL transformations into maintainable code<\/strong>, complete with tests, documentation, and a clear dependency graph.<\/p>\n

The key point is that they do not compete. Snowflake is the engine<\/strong>; dbt is the layer that brings structure<\/strong> on top.<\/p>\n

The team writes models in SQL (or Jinja-SQL when necessary), dbt compiles them and executes them against Snowflake, and Snowflake performs the heavy processing.<\/p>\n

All transformations happen where the data already lives.<\/strong> There is no intermediate movement of data, no transformation server to maintain, and no Python process in the middle manually orchestrating MERGE statements. Everything runs as SQL inside the warehouse, leveraging the power and elasticity that Snowflake provides by default.<\/p>\n

This separation also has implications for team structure: data engineers design models and layers, analytics engineers write business transformations, and everything lives in the same repository following the same standards. There is no separate tool for each role.<\/p>\n<\/div>[vc_empty_space]<\/div><\/div><\/div><\/section>

What You Gain<\/strong><\/h2>\n

Real Modularity<\/strong><\/p>\n

With ref() and source(), dependencies between models are defined without hardcoding schema names anywhere.<\/p>\n

If tomorrow you move from a DEV database to a PROD database, no code changes are required. dbt resolves object names at compile time and automatically builds the DAG. This also means that if an intermediate model is renamed, dbt identifies what is broken before execution.<\/p>\n

Tests as Code<\/strong><\/p>\n

Within the same project where a model is defined, you can declare that a column cannot contain null values, that a key must be unique, or that values must belong to an expected set.<\/p>\n

This is not revolutionary by itself, but having tests live next to the model and execute during every build significantly changes how teams approach data quality<\/strong>.<\/p>\n

With packages such as dbt-expectations<\/strong> or dbt-utils<\/strong>, testing can go much further: statistical distributions, table comparisons, freshness checks, and more. Everything remains declarative and managed through YAML.<\/p>\n

Documentation and Lineage with Minimal Effort<\/strong><\/p>\n

dbt generates a static documentation site containing models, columns, tests, and a navigable DAG<\/strong>.<\/p>\n

For audits, onboarding, or simply understanding what depends on what before refactoring, this is extremely valuable.<\/p>\n

Column descriptions live in the same repository as the code. If someone updates a model without updating its documentation, the team sees it during the pull request process.<\/p>\n

Flexible Materializations<\/strong><\/p>\n

The same model can become a view, table, incremental table, or SCD Type 2 snapshot<\/strong> by changing a single configuration line.<\/p>\n

dbt also takes advantage of Snowflake’s MERGE capabilities for incremental processing, which becomes important when working with large volumes of data.<\/p>\n

For fact tables receiving millions of rows per day, the difference between rebuilding everything and processing only new records can have a significant impact on both execution time and cost.<\/p>\n

Macros and Jinja<\/strong><\/p>\n

When you find yourself writing the same logic across fifteen different models, you can extract it into a macro and reuse it everywhere.<\/p>\n

From deduplication logic to dynamically generated pivots, Jinja turns SQL into something genuinely reusable.<\/p>\n

There is also a large ecosystem of packages available through dbt Hub that solve common problems without requiring teams to reinvent the wheel.<\/p>\n

Separate Environments Without Duplicating Work<\/strong><\/p>\n

With dbt, the same code can target multiple environments (development, staging, production) simply by changing the connection profile.<\/p>\n

Each developer can work within their own Snowflake schema without interfering with others, and promoting code to production becomes a configuration change rather than a manual process.<\/p>\n<\/div>[vc_empty_space]<\/div><\/div><\/div><\/section>

How It Fits into a Layered Architecture<\/strong><\/h2>\n

If you already work with a layered architecture such as staging \/ DWH \/ data marts<\/em>, dbt fits naturally without requiring structural changes.<\/p>\n

A common convention looks like this:<\/p>\n

Staging<\/strong><\/p>\n

One view or table per source, where columns are renamed, data types are cast, and only minimal cleaning takes place.<\/p>\n

There is no business logic here. This layer acts as the contract between source systems and the rest of the project.<\/p>\n

Intermediate<\/strong><\/p>\n

Reusable intermediate steps.<\/p>\n

Joins, aggregations, and enrichments that multiple marts will consume.<\/p>\n

This layer is not always necessary, but as projects grow it prevents marts from becoming overloaded with duplicated logic.<\/p>\n

Marts<\/strong><\/p>\n

Final consumption-oriented models, organized by business domain such as finance, marketing, or operations.<\/p>\n

These are the models consumed by BI tools, data scientists, or application endpoints.<\/p>\n

This structure is not unique to dbt\u2014it is simply good practice\u2014but dbt makes it easier because each layer lives in its own folder with its own tests and documentation.<\/p>\n

The mapping from traditional architectures such as SA \/ DWH \/ DM<\/strong> is straightforward. Staging models correspond to the SA layer, while intermediate and mart models correspond to DWH and DM respectively. The lineage is automatically traced end-to-end.<\/p>\n<\/div>[vc_empty_space]<\/div><\/div><\/div><\/section>

Team Workflow: Git, Pull Requests, and Environments<\/strong><\/h2>\n

One of the least obvious yet most important benefits is the impact on team workflows.<\/p>\n

With dbt, transformations stop being database objects modified directly in production and become code managed in a repository<\/strong>.<\/p>\n

That means:<\/p>\n