New 1.2.1 Release of RA Data Warehouse for dbt, Fivetran, BigQuery, Segment (and now Snowflake DW!)

Mark Rittman

Last year I blogged about our RA Data Warehouse for dbt, a set of data models, data transformations and data warehousing design patterns for dbt (“Data Build Tool”), Fivetran, Stitch, Segment, Google BigQuery that we use to rapidly build-out the data warehouse layer for analytics solutions and data platforms we build for our clients.

RA Warehouse for dbt ArchitectureRA Warehouse for dbt Architecture

RA Warehouse for dbt Architecture

By pre-building data source modules for popular SaaS applications such as Xero, HubSpot, Salesforce, Google Ads and Stripe , connecting them to a common set of routines for combining, deduplicating and integrating each of their datasets we:

  • Deliver projects faster, more efficiently and at lower cost for our clients

  • Deliver work of higher quality, as we’ve already ironed-out all the bugs and refined the code over multiple engagements

  • Deliver more value by spending our time delivering analytics insights, rather than spending our clients’ budgets re-inventing the wheel

  • Provide integrated, best-practice datasets for Marketing Attribution, Product Analytics consulting projects we deliver for clients

Our Conformed, Dimensional Warehouse Data ModelOur Conformed, Dimensional Warehouse Data Model

Our Conformed, Dimensional Warehouse Data Model

What Does the RA Warehouse for dbt Framework Contain?

The core RA Warehouse for dbt framework we’ve open-sourced includes:

  • Pre-built, standardised data source models for popular SaaS applications (Hubspot, Xero, Facebook Ads, Segment etc)

  • Support for Stitch, Fivetran and Segment data pipeline services

  • Google BigQuery and Snowflake data warehouse compatibility

  • Data transformation and integration logic that combines multiple sources, deduplicates and creates single contact and company records

  • Subject-area dimensional warehouses e.g. Finance, Marketing, Product, CRM

  • Utilities for data profiling, ETL run logging and analysis

  • Simple configuration via settings in a single configuration file (dbt_project.yml)

What Use-Cases and Engagement Types does it Enable?

Since our first blog on the framework last year we’ve since deployed and extended the framework on client engagements to:

  • Build a platform and marketing analytics platform for a UK Fintech / Gaming startup

  • Create a web analytics and marketing attribution solution for a US mobile app company

  • Deliver finance and customer analytics for an auction business

  • Deploy NetSuite ERP Retail, eCommerce and Wholesale analytics for a UK retailer

  • Calculate finance, marketing and customer lifetime value analytics for a UK mobile app business

  • Implement an operational analytics solution for a UK/US marketing technology startup

as well as using it as the core of our own internal operational analytics platform, example Looker screenshots below.

Screenshots from Rittman Analytics’ Internal Analytics Platform powered by dbt, BigQuery and LookerScreenshots from Rittman Analytics’ Internal Analytics Platform powered by dbt, BigQuery and Looker

Screenshots from Rittman Analytics’ Internal Analytics Platform powered by dbt, BigQuery and Looker

New in the v1.2.0 Release – Support for dbt 17.0 and Snowflake Data Warehouse

The recently released v1.2.0 release of the RA Warehouse for dbt framework introduces a number of improvements, extensions and new features including:

  1. Refactored and updated data source and transformation logic including updates to reflect changes in dbt 17.0.x

  2. Easier configuration and enablement of data sources and support for Fivetran and Segment as managed data pipeline technology

  3. Support for Snowflake Data Warehouse as the target data warehouse platform

See the What’s New in Version 1.2.0 page in the repo documentation for full details.

Interested? Find out More

You can read more about our work with dbt, Google BigQuery, Snowflake and other modern data stack technologies on our website and blog:

We’ve open-sourced this framework to share our learnings and experience with the dbt and analytics engineering community, and to invite others to review, contribute and fork this repository – go ahead and clone our repo now!

Interested but don’t have the time to do it yourself? No problem! Click here to book a 100% free, no-obligation 30 minute call to discuss your data needs and how we could help get your data analytics project moving now – we’d love to hear from you.