dbt (data build tool) is an open-source tool and accompanying commercial web service that enables data analysts and engineers to transform data in their warehouses more effectively and own the entire analytics engineering workflow; dbtCloud is a cloud service for managing your analytics engineering workflow in one place.

Rittman Analytics is an official Consulting Partner for dbt, working with our clients, the community and the wider dbt ecosystem to get the most out of this open-source analytics framework and accompanying commercial dbtCloud service. Now we’re ready to do the same for you and your business

<aside> 💡 For more examples of how we've used dbt on client and internal projects, check-out our dbt articles on the Rittman Analytics blog

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Who is this Package For?

This package is designed for data teams within an organization who want to learn the basics of dbt and get the first stage of their dbt project delivered.

It assumes that the data team have some basic knowledge of database and data warehouse design principle, have identified the initial data sources for their data warehouse and are reasonably familiar with tools and concepts such as git, unit testing and dimensional modeling.

Objectives and Package Deliverables

We recommend this consulting package be used in-conjunction with formal, classroom or remote-delivered training on dbt from Fishtown Analytics, details of courses can be found here.

What Products and Delivery Tools Do We Use?

The package is based around Google BigQuery and Stitch as the cloud data warehouse and data pipeline technology, and our RA Warehouse dbt package for delivering SaaS data warehouses using dbt, BigQuery and Stitch.

The RA Data Warehouse is a framework for ingesting, combining and restructuring data from multiple source systems into a conformed, dimensional data warehouse.

The framework is based around dbt and pre-built transformations and design patterns taken from Rittman Analytics' previous data warehousing consulting experience and creates a delivery framework that:

  1. Provides standards around how to model and transform various data sources
  2. Makes it simpler to run data quality tests than to not, by defining these tests in-advance
  3. Enables merging of customer, product, contact and other shared entity data with no single authoratitive source