Inside dbt Cloud Architecture: How It Works

Note: This post assumes you’re already familiar with what dbt (data build tool) does at a high level.

Key Takeaways

dbt Cloud is a managed SaaS platform provided by dbt Labs that orchestrates dbt Core workflows for you. This post explores the dbt Cloud architecture, how it handles orchestration, execution environments, metadata, and more.

Understanding dbt Cloud Architecture

Under the hood, dbt Cloud has two categories of components​:

• Static components: Always-on services that make up the control plane – for example, the web application (UI and API) and supporting services that are continuously running to serve user requests.
• Dynamic components: Ephemeral infrastructure that is spun up on demand to do heavy lifting. For example, running a dbt job or provisioning an IDE session. These are ad-hoc containers or processes that exist only for the duration of a task (such as executing a model run or handling an IDE query)

dbt Cloud runs on a multi-tenant architecture by default, meaning many customers share the same platform infrastructure (with logical isolation of data and compute). For enterprises with special needs, a single-tenant (dedicated VPC) deployment is also available with a dedicated VPC for their resources. 

The platform is cloud native, it relies on services like PostgreSQL (for application metadata/backend), object storage (S3) for logs and artifacts, and Kubernetes for managing dynamic execution environments and persistent volumes​. In practice, this means when you run a job or open the IDE, dbt Cloud will likely spin up an isolated Kubernetes pod or similar container to execute your dbt code and use cloud storage to save any logs or result artifacts.

If you’ve only used dbt Core before, this kind of setup might feel like a big shift. This breakdown of dbt Core vs. dbt Cloud covers what changes and what stays the same.

Data Security

dbt Cloud is a control plane for data transformations. It is not storing your warehouse data permanently. Instead, it dispatches SQL to your cloud data warehouse and lets the warehouse do the processing​. Results are fetched only as needed (e.g., for preview or test), kept in memory briefly, and not persisted on dbt Cloud servers​. Each customer’s data and code are isolated. All data at rest is encrypted using AES-256, and all in-transit data is encrypted via HTTPS/TLS​. This design ensures that sensitive customer data remains in the data warehouse (Snowflake, BigQuery, Redshift, etc.) and not in the SaaS application.

Major Components and Features

Cloud IDE

dbt Cloud provides a convenient Integrated Development Environment in your browser. When you open the IDE, dbt Cloud will synchronize your project’s code from your Git repository (GitHub, GitLab, or Azure DevOps) into a temporary environment on the cloud​ (Internally, dbt Cloud caches a copy of your repo to speed up operations and guard against Git provider outages​).

Job Scheduler

dbt Cloud includes a powerful Job Scheduler for automating data transformations in production, eliminating the need for manual CLI runs or cron jobs. It supports flexible scheduling options, including cron-based schedules (e.g., nightly at 2 AM), event-driven triggers (such as jobs running sequentially, one after another), and continuous integration workflows that execute automatically upon merging pull requests. Users can also trigger jobs manually via an API call or a “Run now” button.

Once triggered, the scheduler queues the job and dynamically allocates an execution environment using Kubernetes pods. Each pod is isolated, configured specifically with the user’s dbt project code (pulled from Git), dbt version, and necessary environment variables. Inside the pod, a small agent executes dbt commands (like dbt run or dbt test) directly against the data warehouse.

Real-time logs from the execution are streamed live in the dbt Cloud UI, then saved, along with artifacts (logs, run results, lineage manifests), to cloud storage (S3) for future reference. Upon completion, pods are automatically terminated, maintaining a clean environment.

The Kubernetes-based architecture scales efficiently, allowing multiple concurrent jobs across customers. The platform optimizes job execution by directly creating pods, reducing startup latency and maintaining warm container pools. Users see jobs reliably start and run to completion without dealing with underlying infrastructure complexities.

Metadata and Artifact Management

Every time dbt Cloud runs your project, it produces a plethora of metadata and artifacts – compiled SQL, run results (success/failure, timing), documentation manifest, data lineage info, and so on. dbt Cloud job run produces metadata artifacts such as manifest.json, run_results.json, and catalog.json, stored within your project’s /target directory. The dbt Cloud application persists references to these artifacts in its PostgreSQL back-end so it knows which artifact belongs to which run. This collection of metadata powers the documentation view and lineage graphs you see in the Cloud UI.

Crucially, dbt Cloud also exposes this metadata externally through a GraphQL-based Metadata API (Discovery API). This API makes it possible to integrate dbt’s metadata with other tools such as governance tools, custom dashboards, or data catalogs. This metadata management capability is an integral part of the dbt Cloud architecture, supporting documentation, lineage tracking, and integration with external tools.

CI/CD Workflow and Git Sync

A standout feature of dbt Cloud, especially beneficial for analytics teams, is its built-in Continuous Integration and Continuous Deployment (CI/CD) capabilities. By integrating with Git providers such as GitHub and GitLab through OAuth or access tokens, dbt Cloud seamlessly manages automated workflows triggered by Git events.

For example, when a developer opens pull requests, dbt Cloud automatically initiates CI jobs to build and test only the changed models. These tests are run in isolated schemas, preventing any impact on production data. Results, including compilation and test outcomes, are reported directly back into the pull request via status checks, enabling early detection of errors and reducing integration risks.

For Continuous Deployment, dbt Cloud supports automated “merge jobs” that trigger immediately upon merging code into the main branch, promptly updating production models. Alternatively, production deployments can be scheduled using a reliable cron-based scheduler. Additionally, environment management features allow teams to clearly define and separate staging from production environments, ensuring secure and correct database connections and schema targets for each job.

Further enhancing the workflow, dbt Cloud provides notifications via email or integrations like Slack to inform teams about job statuses and failures in real-time.

Beyond its internal orchestration, dbt Cloud offers flexibility through Administrative and Metadata APIs. These enable external orchestration tools, such as Apache Airflow or Prefect, to trigger or listen for job events, making dbt Cloud adaptable within broader data pipelines. Ultimately, dbt Cloud simplifies analytics workflows by eliminating the need for external CI/CD tools, streamlining both development and deployment processes.

Interfacing with Cloud Data Warehouses

dbt Cloud is built to work hand in glove with modern cloud data platforms. It doesn’t replace your data warehouse, instead it coordinates work within it. Let’s see how dbt Cloud interfaces with popular warehouses like Snowflake, BigQuery, and Redshift:

• Connections and credentials: In dbt Cloud, you define a connection for your warehouse (providing credentials such as a Snowflake user/key or a BigQuery service account, etc.). These are stored securely on the platform. When an IDE session or job run starts, it uses these credentials to connect directly from the container to your warehouse over the network.
• Executing transformations: If you schedule a job to run a set of models, what happens is the dbt process inside the container connects to (for example) Snowflake using the provided account, then issues “CREATE TABLE“, “AS SELECT“, or “MERGE” statements as defined by your model logic. Snowflake will execute those SQL statements internally (utilizing its own compute). The dbt process monitors for completion and then moves to the next model. The key point is that all your data remains in Snowflake/BigQuery/Redshift during this, only the SQL and small metadata results travel through dbt Cloud.
• Multi-warehouse support: dbt Cloud can connect to a variety of databases because it’s essentially running dbt Core under the hood, and dbt Core has adapters for many platforms. Snowflake, BigQuery, Redshift are common, but it also supports Databricks, Postgres, Azure Synapse, etc., as long as an adapter is available. 
• Warehouse roles and schemas: In practice, users often configure their dbt Cloud jobs to target specific schemas (like a “dev” schema for development runs and a “prod” schema for production runs). The credentials used might have permissions scoped to certain schemas or roles.  The platform doesn’t bypass any of the warehouse’s security controls; it actually relies on them. So, if a model tries to select from a table that it’s not allowed to, the warehouse will throw an error.
• Performance considerations: Dbt Cloud runs queries directly in your data warehouse, with minimal scheduler overhead and negligible network latency due to regional proximity. However, performance depends on your warehouse’s capacity and concurrency limits. You can control query parallelism in dbt Cloud by adjusting thread settings, allowing you to balance throughput and warehouse load effectively.

Typically, the flow often looks like this: an ingestion tool loads raw data into the warehouse, then dbt Cloud transforms that raw data into modeled tables, and finally, BI or analytics tools consume those models.

For example, you might use Hevo Data to continuously replicate data from source applications into Snowflake. Previously, you could schedule Hevo to finish its batch by 1 AM and then schedule dbt Cloud to kick off at 1:15 AM to transform the fresh data. However, with Hevo Transformer (powered by dbt Core), the transformation step can now happen directly within the Hevo pipeline, right after ingestion is complete, removing the need to manage separate scheduling between tools.

In fact, to ensure seamless coordination, some teams still integrate dbt Cloud (or Hevo Transformer) with their orchestration tools: if Hevo (or any ETL pipeline) signals that loading is complete, you can trigger the transformation job via API immediately. This way, your transformations always run after new data is landed, avoiding any timing gaps. Conversely, once dbt Cloud or Hevo Transformer finishes a run, you could use webhooks or APIs to notify downstream systems (like refreshing a dashboard or triggering a machine learning job to pick up new data).

Conclusion

dbt Cloud takes care of the heavy lifting, with no infrastructure headaches, no manual scheduling, just clean, tested, and version-controlled analytics code that runs like clockwork. Whether you’re collaborating in the web IDE, tracking runs with the scheduler, or scaling transformations seamlessly across environments, dbt Cloud makes it all feel effortless.

But writing great dbt models is just one piece of the puzzle.

If you’re looking to simplify how you orchestrate, manage, and trigger these models within your broader data pipeline, meet Hevo Transformer. It integrates natively with dbt Core and helps you automate model execution post-load, track lineage, and manage transformations directly from your data flow without switching tools.

Want to see how it works in action?

Start for free with Hevo Transformer and level up your dbt transformation workflow. No engineering support required.

Frequently Asked Questions