dbt
dbt consulting for tested, trusted analytics models
dbt consulting helps data and analytics leaders replace fragile SQL scripts and stored procedures with version-controlled, tested models everyone can trust. We set up and refactor dbt projects on Snowflake, Databricks, BigQuery, Redshift and other warehouses for mid-size and large US companies. Work starts with a free review and a fixed quote, ships in stages, and lives in your own Git repository.
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Quick answer
What is dbt used for?
dbt is used to transform raw data inside a data warehouse into clean, tested tables for reporting and analysis. Teams write SQL models, and dbt runs them in dependency order, tests the results and documents lineage. It works on Snowflake, Databricks, BigQuery, Redshift and other warehouses, alongside separate ingestion tools like Fivetran.
- dbt transforms data already in the warehouse; it does not extract or load it.
- dbt Core is free and open source; dbt Cloud adds hosting, CI and SSO.
- Tests, CI and lineage let teams define each business metric once and trust it.
- Stored procedure migrations ship in stages with old and new outputs reconciled.
01
What is dbt and what dbt consulting covers
dbt (data build tool) is the transformation layer of the modern data stack. Analysts and engineers write SELECT statements with Jinja templating, and dbt compiles them, works out dependencies, builds tables and views in the warehouse in the right order, runs tests and generates documentation with lineage. It does not extract or load data; it transforms data that tools like Fivetran, Airbyte or custom pipelines have already landed.
Our dbt consulting work covers the full project lifecycle, from first setup to cleaning up a project that has grown to hundreds of models with no conventions.
- Project structure with staging, intermediate and mart layers and clear naming
- Sources, freshness checks, data tests and unit tests
- Incremental models and snapshots for slowly changing dimensions
- CI/CD that builds and tests only changed models on each pull request
- Documentation, lineage and ownership for every model
- Semantic layer metrics so BI tools share one definition of revenue or churn
02
Analytics engineering in practice
Analytics engineering applies software practices (version control, code review, testing, environments) to the SQL that turns raw data into business tables. It sits between data engineering, which moves data reliably, and analysis, which answers questions with it.
In practice this means a revenue figure is defined once in a tested model, reviewed in a pull request and documented, rather than rebuilt differently in five dashboards. When finance and sales disagree on a number, the lineage graph shows exactly which source and logic produced each one.
WHAT WE BUILD
What we automate
Typical automations, each scoped and quoted at a fixed price after a free review.
Pull request CI builds
Every pull request builds and tests only the changed models and their downstream dependencies in a temporary schema before anyone can merge.
Scheduled production runs
dbt Cloud jobs or an Airflow or Dagster DAG run models after each ingestion load finishes, rather than at a fixed clock time.
Source freshness alerts
Freshness checks flag when a Fivetran or Airbyte sync falls behind and notify the data owner in Slack or Teams.
Test failure tickets
Failed uniqueness, not-null or business-rule tests open a ticket in Jira or ServiceNow with the model, owner and failing rows attached.
Stored procedure conversion
Legacy SQL Server, Oracle or Teradata procedures are rewritten as modular dbt models and reconciled against the old output.
Documentation publishing
Model docs and lineage regenerate on each deploy so analysts always see current definitions and dependencies.
03
dbt Cloud vs dbt Core
dbt Core is the open-source command-line tool. It is free, and you run it yourself through an orchestrator such as Airflow, Dagster, GitHub Actions or a scheduled container. It suits teams with engineering capacity who want full control of how and where jobs run.
dbt Cloud is dbt Labs' hosted platform, now marketed as the dbt platform. It adds a browser IDE, managed job scheduling, CI on pull requests, hosted documentation and catalog views, the semantic layer service, SSO and role-based access. It is licensed per seat and plan. We help you choose based on team size, security requirements and existing orchestration, and we can migrate in either direction.
- Choose dbt Core when you already run Airflow or Dagster and have engineers to maintain it
- Choose dbt Cloud when analysts need a browser IDE and you want managed CI, scheduling and SSO
- Either way, the SQL models are portable between the two
04
dbt consultant vs dbt developer
A dbt consultant decides how the project should be organized: layers, naming, testing standards, environments, deployment and how dbt fits with ingestion and BI. A dbt developer writes the models, macros and tests inside those standards.
Enterprise teams often bring us in when one of three things happens: a legacy warehouse full of stored procedures needs to move into dbt, an existing dbt project has become slow and hard to change, or several teams need to share models without breaking each other. For larger organizations we can split projects by domain with cross-project references, so each team owns its models and publishes stable interfaces to others.
05
dbt on Snowflake, Databricks and BigQuery
dbt runs on top of your warehouse through adapters, so the same approach works across platforms with small differences. On Snowflake we tune warehouse sizes per job and use clustering where it helps. On Databricks we write to Unity Catalog and choose between SQL warehouses and Spark for heavier models. On BigQuery we use partitioning and clustering to keep scanned bytes and cost down.
We also connect dbt to the rest of your estate: source data from SAP S/4HANA, Salesforce or ServiceNow, governance tags that flow into your catalog, and marts that feed Power BI, Tableau or Looker. Data quality failures can open tickets automatically so the right owner sees them.
06
What dbt consulting costs
We quote a fixed price after a free review of your warehouse, existing SQL and reporting needs. Cost depends on the number of sources and models, how much legacy logic needs translating, testing and CI requirements, and whether a semantic layer is in scope.
A new dbt project with a first set of tested marts typically ships in 3-4 weeks. Refactors and stored procedure migrations are delivered in stages over 4-8 weeks, with old and new outputs reconciled side by side before anything is switched off. dbt Cloud licenses, if used, are billed by dbt Labs.
TOOLS
Tools we connect
FAQ
dbt Consulting questions
What does a dbt consultant do?
A dbt consultant sets up or improves your dbt project: structure, naming, tests, CI/CD, documentation and deployment. Most engagements also include building or refactoring models so business metrics are defined once and trusted across teams.
Is dbt Core free?
Yes. dbt Core is open source and free to use. You pay for the warehouse compute it runs on and whatever you use to schedule it. dbt Cloud is a separate paid hosted product.
Should we use dbt Cloud or dbt Core?
Use dbt Core if you already run an orchestrator and have engineers to maintain the setup. Use dbt Cloud if analysts need a browser IDE, or you want managed CI, scheduling, SSO and the hosted semantic layer without running infrastructure.
Does dbt replace Fivetran or Airflow?
No. dbt handles transformation inside the warehouse. Fivetran or Airbyte load the data, and Airflow or Dagster can orchestrate the whole flow, including dbt runs.
How long does it take to migrate stored procedures to dbt?
It depends on how many procedures exist and how tangled they are. We migrate in stages by business area, reconciling outputs at each step, so a first area typically moves in a few weeks rather than waiting for one large cutover.
Can dbt work with Power BI?
Yes. dbt builds clean mart tables in your warehouse, and Power BI reads from them. This keeps business logic in tested SQL instead of spread across Power Query steps and DAX.
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- Fixed quote before any work starts
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