Managed dbt that lets you focus on data

Datacoves is an end-to-end data engineering platform that unifies dbt, Airflow orchestration, Python development, governance, and best practices. It can be deployed in a customer’s environment and provides a secure, managed workspace.

Yes. Standardized environments and templates reduce onboarding time by ~30 hours per developer.

Yes. AI coding tools run inside the browser VS Code environment: Claude Code, GitHub Copilot, Snowflake Cortex CLI, OpenAI Codex CLI and Gemini CLI, with MCP servers supported. You choose which LLM backs them, so the model stays your decision rather than the platform’s. Teams use them to generate dbt tests, documentation, models and DAGs, and to explain errors.

Datacoves runs on modern browsers such as MS Edge and Google Chrome. No special requirements are needed on the user’s computer. This is what allows us to get new users up and running in minutes. Deliver insights and leave the infrastructure to us.

Datacoves users follow standard dbt development practices. New features are created on a branch, they are then pushed to a git server like GitHub where they are tested. Once the change is approved, the branch is merged into the repo’s main branch. The main branch is picked up by Airflow and jobs run as scheduled.

Datacoves runs on Kubernetes, either inside your own cloud account on AWS, Azure or GCP, or as multi-tenant SaaS. A private deployment installs into the account you already have and needs no VPC peering.

DIY gives you flexibility but costs you time, consistency, and institutional knowledge. Teams that go that route spend months wiring together Airflow, dbt, Python environments, secrets management and CI/CD, then more months maintaining it as versions change. Datacoves delivers all of that preconfigured and managed.

When previewing queries in our IDE the resulting data passes through our infrastructure on the way to your browser, but we do not store it, and by default we limit query previews to 500 lines.

Yes. Datacoves has helped large organizations manage tens of dbt projects in a single organization, with hundreds of developers, hundreds of Airflow DAGs, and thousands of dbt models.

No, Datacoves is built on open standards and tools. You own your data, your warehouse, your git repository, and all your configurations. You can host your own infrastructure and create what we have, but trust us, it’s not as simple as it sounds.

My Airflow is a personal sandbox for testing. Teams Airflow is shared orchestration for production workflows.

Datacoves runs a full VS Code IDE in the browser, so developers get a consistent environment without installing anything locally. It comes with dbt, Python virtual environments, Git and SQLFluff configured, and you can install any Python library or VS Code extension. AI coding tools run inside it with the LLM you choose.

Teams move to Datacoves from Informatica, SSIS, Alteryx, Talend, Matillion, Coalesce.io and homegrown ETL. The pattern is the same in each case: the logic built in a GUI gets rewritten as dbt models under version control, and the scheduling moves into Airflow. Pipelines in other systems can be orchestrated from Airflow while the migration runs.

The recurring themes are faster onboarding for new engineers, more reliable pipelines, less time spent maintaining platform infrastructure, and governance that holds without manual review. See case studies for what specific teams have said.

Datacoves eliminates the complexity of building and maintaining dbt, Airflow, Python, CI/CD, and secrets management. It provides a unified, secure, governed environment.

Datacoves supports any available warehouse feature including Snowflake cloning, incremental builds, dynamic tables, and any dbt features.

Analytics engineers, data engineers and platform teams use Datacoves day to day, in the browser-based VS Code environment and in Airflow. The people who buy it are Heads of Data, Directors of Analytics, VPs and CTOs at large enterprises, most often in regulated industries: pharma and life sciences, healthcare, financial services, retail, manufacturing and government. Teams tend to come to us when a SaaS-only tool can’t meet a security or data residency requirement, or when running dbt and Airflow themselves has turned into its own project. See case studies for how teams use it.

Yes. Datacoves is an alternative to dbt Cloud, now the dbt Platform. It runs Managed dbt and Managed Airflow in your own cloud, with VS Code in the browser, pre-built CI/CD pipelines, secrets management and governance guardrails. Teams pick it when a SaaS deployment doesn’t fit: private deployment in their own cloud, data residency rules, or a security review that rules hosted tooling out. See the dbt Cloud comparison.

While Datacoves is not open source, it is built on open components to which we also contribute. There is no vendor lock-in as customers can recreate what we have done, but trust us, this is not simple.

Snowflake, Databricks, BigQuery, Redshift and others. Datacoves works with any warehouse that has a dbt adapter, open source or proprietary.

Yes. Datacoves runs dbt tests and dbt unit tests as part of every build, lints SQL with SQLFluff, and enforces governance checks in the pre-built CI/CD pipelines. Failures surface through orchestrated alerts in Airflow rather than being found by whoever opens the dashboard next.

Yes. Datacoves Mesh handles multi-project and multi-team setups, so separate teams own their own projects and still reference each other’s models with lineage across them. It’s our own implementation, covering the same ground as dbt Mesh. Every plan includes unlimited projects and environments.

Datacoves connects to your existing Git repository, and works with GitHub, GitLab, Bitbucket and Azure DevOps. The browser VS Code environment is wired to it, so developers branch, commit and open pull requests without any local setup. Airflow syncs DAGs from the same repository.

Datacoves includes My Airflow for development and Teams Airflow for production. It syncs DAGs from Git, supports secrets, and includes dbt operators and decorators.

The pipelines come pre-built rather than something your team assembles. They run dbt tests, SQL linting, governance checks, documentation builds and deployment, and you can extend them or run them on your own CI provider.

Datacoves provides a managed dbt runtime, environments, CI/CD, testing frameworks, documentation, and Git-driven workflows.

Any BI tool that connects to your warehouse works, because Datacoves transforms data in the warehouse and leaves it there. Teams commonly use Tableau, Power BI, Looker, Omni and Excel. There is nothing to integrate on the Datacoves side: point the BI tool at the same warehouse.

Don’t hesitate to reach out to us from the contact us form in the footer below.

Datacoves does not require a particular ingestion tool. Keep the one you already use, such as Fivetran or Airbyte, and orchestrate it from Airflow alongside your transformations. For code-first loading, teams use dlt (dltHub), custom Python, or Cartage, our open-source declarative loader.

Every customer gets onboarding, architecture guidance and a shared success channel with our team rather than a ticket queue. Support covers how you use the platform as well as whether it is running: dbt modelling questions, Airflow DAG design, CI/CD setup and version upgrades. Teams that want help building the first version of a platform can start with a Datacoves Foundation engagement, which delivers an MVP in about six weeks of build time and leaves them owning everything it produces.

Secrets are stored in AWS Secrets Manager or customer vaults and securely injected into workflows.

No. Datacoves uses open-source dbt and ensures full ownership of code and metadata.

Governance is enforced in the workflow rather than by policy documents. Every change moves through Git with branching standards, and the CI/CD pipelines block a merge that fails the checks. Secrets stay in AWS Secrets Manager or your own vault, and deployment guardrails control what reaches production.

Yes. Since Datacoves can be deployed in a private cloud account, it is ideal for pharma, healthcare, finance, and government.

Learn what we do with your data here. We also understand that SaaS is not for everyone so we built Datacoves to be deployed in your private cloud. Customers with GDPR and sensitive data requirements sometimes choose to run Datacoves within their cloud account (Azure, AWS, or GCP).

Your data and credentials stay in infrastructure you control when Datacoves runs in your own cloud account. It integrates with your identity provider for SSO and SAML, runs with VPC isolation, and records audit logs of platform activity. Secrets are held in AWS Secrets Manager or your own vault.