dbt Cloud 🤝 Google Cloud

dbt Cloud now available on Google Cloud

If you’re running dbt Cloud on BigQuery – and we know many of you are – today’s announcement that dbt Cloud is now available on Google Cloud is super exciting. It means your data control plane and your data platform are now both hosted natively on the Google ecosystem. Today our customers have even more flexibility in how they build and scale their data and AI environments. Whether you’re centralizing spend, focused on compliance and data residency, or building data pipelines, you now have all the power of dbt Cloud—on Google’s trusted infrastructure.

If you run your data architecture on Google Cloud, but are not yet a dbt customer, you can now find and purchase dbt Cloud directly from the Google Marketplace. That means you can get up and running faster, simplify procurement, and align spend with your existing cloud commitments.

For those unfamiliar, dbt Cloud is a Data Control Plane that helps organizations build and manage their pipelines, so teams can deliver high-quality, trusted data to the business faster and at a reduced cost. dbt takes a code-first approach, with a variety of governed development environments that cater to a range of technical aptitudes. This includes a cloud-connected command-line interface, a graphical IDE, an AI-powered Copilot, or a visual drag-and-drop experience – it’s all interoperable. Data teams regularly build up data warehouses with dbt: dimensional modeling, data vault, data marts, or one big table. Whether the end application is AI, Looker for BI, or operational analytics, dbt is the new standard for data transformation and provides a solid foundation on which organizations to build revenue-generating data products.

Check out the Dev Day blog to learn about the new features of dbt Cloud that are turbocharging data development.

Why dbt Cloud on Google Cloud?

For many teams, data architectures are getting more complex. There's a growing need to deploy workloads flexibly, meet region-specific data residency requirements, and manage cost—without sacrificing performance. By bringing dbt Cloud to Google Cloud, we’re giving customers the ability to deploy where it makes the most sense for their business. And since we already support deep integrations with BigQuery you’ll be able to run fast, efficient transformation workflows on one of the most scalable engines out there. This includes new enhancements like full support for BigQuery DataFrames, so Python workflows and machine learning pipelines can live right alongside your SQL models.

BigQuery customers that are recognizing business value from dbt Cloud include RocketMoney, Bilt Rewards, and Virgin Media O2.

There are a number of advantages to building your data pipelines in BigQuery with dbt Cloud:

Faster Time to Value - Build data pipelines using modular SQL-focused code, CI/CD, versioning, and scheduling to speed development and reduce errors. dbt now supports BigQuery DataFrames, so you can execute dbt python models directly on BigQuery and Dataproc.

Increased Governance - Built-in data testing, automated documentation, and detailed data lineage ensure that transformed data in BigQuery is accurate, reliable, and easy to audit.

User empowerment - With governed development environments that appeal to any level of technical acuity - Cloud CLI, graphical IDE, or drag & drop visual editor - dbt Cloud makes it easier for analysts to collaborate and bring critical context to data development.

Scalability + management - Out of the box Incremental models, partitioning, and clustering help reduce data scanned and improve efficiency.

Platform flexibility

dbt Cloud now works on all the major hyperscalers and most popular cloud data platforms. With out-of-the-box support for the Iceberg open table format (coming soon), dbt gives you greater choice in where you store your data and where you work with it, eliminating the need for you to extract and load data into the platform directly. For example, transformed data stored in Google Cloud Storage in Iceberg format can be read by multitudes of applications and cloud data platforms (Snowflake, Databricks, Redshift) – using the tools or platform that best suits your team needs and requirements. For companies with multi-cloud or multi-platform environments, or who empower domain teams to manage their own pipelines, dbt and Iceberg provide a common way of working across projects enforced by data contracts. This enables teams to move faster while staying within governance and compliance boundaries.

Caption: cross-project and cross-platform references in dbt Cloud

AI runs on data. Data runs on dbt.

You’re probably familiar with the old proverb in computer science, “garbage in, garbage out.” Organizations use dbt Cloud to produce higher quality datasets with data quality checks at every step of the data pipeline: from source testing and freshness checks on the raw data, all the way to data contracts and tests at the consumption layer.

Inspired by how software developers work, dbt adds testing, documentation, and versioning to ensure datasets are high quality and reusable for years to come. dbt Copilot, the AI experience embedded within the data control plane, helps data teams leverage the power of GenAI with their rich metadata context to speed up analytics development across dbt Cloud. This includes AI to auto-generate documentation, data tests, semantic models, metric definitions, and inline SQL. With dbt Copilot, data teams have a context-aware, AI assistant directly in the dbt Cloud IDE—and soon, the visual editor – that leverages dbt model structures, relationships, metadata, and lineage to deliver smart, actionable recommendations.

Caption: dbt Copilot autogenerating documentation

When data is well-governed and created with confidence, it improves the quality and veracity of AI outputs and gets us closer to finally realizing value from AI.

With dbt’s python models, running data science workloads and machine learning during the process, provides a reliable way to bring data warehouse datasets downstream for training and running models using an MLOps approach. dbt Cloud also integrates with Vertex, allowing dbt to query BigQuery with Vertex AI functions, to apply key insights to data, and store them back into BigQuery, for other applications to pick up on and easily build on.

We're co-hosting a webinar with the Google Cloud team on May 7 to walk through everything that’s new, complete with demos and tips for getting started.