![Alex S.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Alex S.") AS Alex S. Consultant Data Architect Enterprise (\> 1000 emp.) 7/16/2026 "Datacoves Delivers Stable, Managed Airflow with Seamless dbt + Snowflake Integration" 5/5 What do you like best about Datacoves? Datacoves gives us a managed Airflow on Kubernetes deployment without the operational burden of building and maintaining that infrastructure ourselves. We get full control over DAGs, connections, and variables, while Datacoves handles the underlying Kubernetes orchestration, scaling, and upgrades. The prewired integration between Airflow, dbt, and Snowflake removes a lot of the plumbing work you'd otherwise spend weeks on when standing up a similar stack from scratch. The in-browser VS Code environment is a nice touch, giving developers a consistent, ready-to-go setup (git, dbt, Python virtual environments, SQLFluff) without local machine configuration, and the Airflow UI itself is the standard, familiar interface, so there's no learning curve if you already know Airflow. We've built a fairly complex generic ingestion framework (S3 to Snowflake, incremental loads, SCD2, manifest and data file pairing) on top of it, and the platform has been a solid, stable foundation for that work. It's also flexible enough to support custom tooling on top, like a Streamlit-based troubleshooting frontend and custom email alerting for pipeline failures, without fighting the platform. On the support side, we have weekly meetings with one of the co-founders, which has been genuinely useful, both for quick troubleshooting and for getting direct product input rather than going through a generic support queue. That level of access is unusual and has made onboarding and ongoing issue resolution noticeably smoother. Review collected by and hosted on G2.com. What do you dislike about Datacoves? One area with room to grow is My Airflow, the developer sandbox instance. It's useful for basic DAG testing, but bringing it closer to feature parity with Teams Airflow would make it a stronger environment for iterating on more complex DAGs before promoting to production. Review collected by and hosted on G2.com. What problems is Datacoves solving and how is that benefiting you? Before Datacoves, standing up a scalable Airflow environment meant building and maintaining our own Kubernetes infrastructure, a significant engineering effort on its own before we could even start writing pipelines. Datacoves solves that by giving us a managed Airflow on Kubernetes deployment out of the box, letting us focus entirely on building our ingestion and orchestration logic instead of infrastructure. Combined with the prewired dbt and Snowflake integration, this let us build a fairly complex generic ingestion framework (S3 to Snowflake, incremental loads, SCD2, manifest and data file pairing) without spending time on the underlying platform work. The result has been faster iteration on pipeline logic and less time spent on infrastructure maintenance and upgrades. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerSource: Organic See what 19 reviewers think of Datacoves 4.8 out of 5 ยท Verified reviews from real users [ Read all reviews ](https://www.g2.com/products/datacoves/reviews)