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DBT LABS CONSULTING PARTNER

dbt Labs Consulting Partner in Australia

We help analytics and data teams adopt dbt the right way: modular SQL models, automated tests, documentation and CI/CD, so every number in your reports can be traced, trusted and changed safely.

Talk to a dbt specialist How it fits together
dbt Labs and DATA LEAGUE logos
dbt Labs partner
SQL-firstAnalysts build models in a language they know
TestedData tests run on every change
PortableSnowflake, Databricks, Fabric, BigQuery and more
PerthBased, working Australia-wide

Why dbt?

dbt (data build tool) brings software engineering habits to analytics. You write transformations as plain SQL SELECT statements, and dbt builds them in the right order, tests the results, documents them and shows the lineage from source to report.

The result is a single, version-controlled home for business logic, instead of rules scattered across stored procedures, ETL jobs and report formulas.

Is dbt right for you?

We'll give you a straight answer, even when it's “not yet” or “not this one”.

A strong fit when

  • Transformation logic is scattered across stored procedures, ETL tools and report formulas
  • Analysts know SQL and want to own their models, not wait in an engineering queue
  • You need to show where a number came from, with lineage, documentation and tests
  • You're on a cloud platform such as Snowflake, Databricks, Fabric or BigQuery

Worth a second look when

  • Most of your work is loading data: dbt transforms data already in your platform, so you'll still need an ingestion tool
  • Your team hasn't used Git before. That's fine, but plan for some training (we can help)
  • A handful of small datasets and simple reports may not need the extra structure

How a dbt project fits together

A well-structured dbt project moves data through clear layers, each with its own job. Select a layer to see what happens there, and what we do.

Stage 1 of 5: Sources

Raw tables already loaded into your warehouse or lakehouse.

What we doWe declare sources with freshness checks, so stale data is caught before reports refresh.
  • Sources
  • Freshness checks
Version control and code review on every change, so nothing reaches production untested.

What We Deliver with dbt

dbt setup

Project structure, environments and conventions on dbt Core or the dbt platform (formerly dbt Cloud).

Legacy logic migration

Stored procedures and legacy ETL rebuilt as dbt models, reconciled against the old results.

Testing and data quality

Generic and custom tests, source freshness checks and alerts to the right people.

Semantic Layer and metrics

Key metrics defined once and used consistently across reports and tools.

CI/CD and DataOps

Pull-request checks, deployment environments and scheduled jobs, on Azure DevOps or GitHub.

Training and enablement

Hands-on workshops, so your analysts can build and maintain models with confidence.

dbt Use Cases

Typical problems we solve with dbt, and what changes when they're solved.

Local government

Replacing a tangle of stored procedures

The challenge
Reporting logic lives in hundreds of stored procedures that only one person fully understands.
What we build
dbt models rebuilt layer by layer, reconciled against the old outputs, with tests and documentation for each.
The outcome
Logic anyone on the team can read, test and change, and a lineage graph to prove it.
  • dbt
  • SQL Server
  • Azure DevOps
Hospitality

One definition of occupancy and RevPAR

The challenge
Different reports calculate occupancy and revenue per available room differently, and meetings start with debates about whose numbers are right.
What we build
Metrics defined once in the dbt Semantic Layer and used by every dashboard.
The outcome
The same numbers everywhere, so meetings start with decisions instead of reconciliations.
  • Semantic Layer
  • Marts
  • Power BI
Any industry

Catching bad data before the board pack does

The challenge
Data issues are found by executives reading reports, not by the data team.
What we build
Freshness, uniqueness and accepted-value tests that run before reports refresh, with alerts to data owners.
The outcome
Problems are caught and fixed before anyone sees them, and trust in the numbers goes up.
  • dbt tests
  • Source freshness
  • Alerts

Facing something similar? Talk to us about your use case.

Works Well With

Snowflake One of the most popular platforms to run dbt on.
Databricks dbt models on Databricks SQL and Delta Lake.
Microsoft Fabric dbt on Fabric warehouses and lakehouses.
DevOps & DataOps CI/CD and automated deployment for data.

Thinking about dbt?

Whether you're starting fresh or untangling legacy logic, tell us about your setup and we'll suggest a practical first step.

Talk to us Managed services

Frequently Asked Questions

dbt (data build tool) is a transformation framework. You write SELECT statements, and dbt turns them into tables and views in your warehouse, in the right order, with tests, documentation and lineage. It's available as open-source dbt Core and as dbt Labs' managed dbt platform (formerly dbt Cloud).

dbt Core is free and runs anywhere you can run Python, but you manage scheduling, CI and hosting yourself. The dbt platform adds a browser-based IDE, scheduling, CI, the Semantic Layer and hosted documentation for a licence fee. We'll recommend one based on your team size and existing tools.

Most modern platforms, through adapters, including Snowflake, Databricks, Microsoft Fabric, Google BigQuery, Amazon Redshift and PostgreSQL.

No. dbt transforms data that's already in your platform. Loading is handled by tools such as Azure Data Factory, Fivetran, Snowpipe or Databricks Auto Loader, and dbt takes over from there.

If they know SQL, they're most of the way there. The new parts are Git, a little Jinja templating and the dbt project structure, which we cover in hands-on training.