100M+

Production data jobs executed on Conveyor since launch

100M+

Production data jobs executed on Conveyor since launch

Your data pipelines are code. They just don't know it yet.

Fully managed DataOps platform for shipping data as code.

We do the heavy lifting, so your data team can focus on the business logic.

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Dashboard Image
Dashboard Image

Trusted by

  • imec-logo
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    luminus-logo

Key Benefits

Key Benefits

Key Benefits

Ship faster, ship better

Faster time to production, fewer deployment errors and more time for your data team to focus on delivering business value

Ship faster

Ship faster

Get to production 3-5x faster with less manual work. Standardized workflows and automated deployment eliminate the repetitive setup work that slows teams down. Engineers onboard in hours instead of days because every project follows the same pattern.

Deploy with confidence

Efficient Planning and Real-Time Tracking

Plan tasks, set priorities, and track progress with ease to ensure projects stay on track and deadlines are met with minimal effort.

Focus on business value

Focus on business value

Cut time spent on infrastructure, Kubernetes configs, and tooling integration. Fully managed means truly hands-off, automatic updates, monitoring, and maintenance included. Conveyor handles the operational complexity so your team can focus on solving business problems.

Ship faster

Ship faster

Get to production 3-5x faster with less manual work. Standardized workflows and automated deployment eliminate the repetitive work that slows teams down. Engineers onboard in hours instead of days because every porject follows the same pattern.

Ship faster

Ship faster

Get to production 3-5x faster with less manual work. Standardized workflows and automated deployment eliminate the repetitive work that slows teams down. Engineers onboard in hours instead of days because every porject follows the same pattern.

Ship faster

Ship faster

Get to production 3-5x faster with less manual work. Standardized workflows and automated deployment eliminate the repetitive work that slows teams down. Engineers onboard in hours instead of days because every porject follows the same pattern.

Deploy with confidence

Deploy with confidence

Reduce deployment errors and production incidents. Test in production-like environments before promoting to prod. Standardized practices across all projects mean fewer surprises and easier debugging. Roll back instantly if something breaks.

Deploy with confidence

Deploy with confidence

Reduce deployment errors and production incidents. Test in production-like environments before promoting to prod. Standardized practices across all projects mean fewer surprises and easier debugging. Roll back instantly if something breaks.

Deploy with confidence

Deploy with confidence

Reduce deployment errors and production incidents. Test in production-like environments before promoting to prod. Standardized practices across all projects mean fewer surprises and easier debugging. Roll back instantly if something breaks.

Focus on business value

Focus on business value

Cut time spent on infrastructure, Kubernetes configs, and tooling integration. Fully managed means truly hands-off, automatic updates, monitoring, and maintenance included. Conveyor handles the operational complexity so your team can focus on solving business problems.

Focus on business value

Focus on business value

Cut time spent on infrastructure, Kubernetes configs, and tooling integration. Fully managed means truly hands-off, automatic updates, monitoring, and maintenance included. Conveyor handles the operational complexity so your team can focus on solving business problems.

Focus on business value

Focus on business value

Cut time spent on infrastructure, Kubernetes configs, and tooling integration. Fully managed means truly hands-off, automatic updates, monitoring, and maintenance included. Conveyor handles the operational complexity so your team can focus on solving business problems.

Abstract
Abstract

What Users Are Saying

Conveyor frees up our platform team. We don't need to worry about compute management or version upgrades.

Jelle D.

Platform Engineer, Team Lead

Creating new data pipelines is efficient, deployment is efficient, and the cost tracking is incredibly useful. The entire workflow just works.

Wim B.

Data Engineer, Team Lead

Conveyor is the backbone of our data operations. It connects GitLab, AWS, Airflow, and our access controls into one integrated workflow. Everything revolves around it.

Jos T.

Data Engineer

Running and deploying DAGs is straightforward with Conveyor. That simplicity means I can focus on the functional requirements of my projects instead of deployment mechanics.

Alex L.

Data Engineer

Conveyor lets me focus on what matters, solving business problems. I don't have to figure out deployment tech stacks anymore.

Oliver W.

Data Engineer, Team Lead

Conveyor is at the core of our Neo data platform at Luminus. All our data use cases run on it, whether they're Spark jobs or dbt models executing in Snowflake.

Mounes B.

Data Engineer

Our Story

Our Story

Our Story

Why Conveyor

Why Conveyor

Why Conveyor

We built Conveyor to bring engineering best practices to data work. Standardized deployment and version control, yes. But also: observability for catching data quality issues early, cost tracking for expensive Spark jobs, easy rollback when bad data gets through.

Leading data teams ship not just faster, but also better. That only happens when best practices are embedded into your data platform.

Pascal Knapen, CTO and Head of Product at Dataminded

We built Conveyor to bring engineering best practices to data work. Standardized deployment and version control, yes. But also: observability for catching data quality issues early, cost tracking for expensive Spark jobs, easy rollback when bad data gets through.

Leading data teams ship not just faster, but also better. That only happens when best practices are embedded into your data platform.

Pascal Knapen, CTO and Head of Product at Dataminded

We built Conveyor to bring engineering best practices to data work. Standardized deployment and version control, yes. But also: observability for catching data quality issues early, cost tracking for expensive Spark jobs, easy rollback when bad data gets through.

Leading data teams ship not just faster, but also better. That only happens when best practices are embedded into your data platform.

Pascal Knapen, CTO and Head of Product at Dataminded

About Image
About Image
About Image

500+

Enterprise Users

Enterprise Users

1.5M+

Jobs/Month

Jobs/Month

1k+

Data Pipelines

Data Pipelines

98%

Client Satisfaction

Client Satisfaction

Product Overview

Engineering-grade foundations for data teams

Conveyor combines compute and orchestration into a single platform, built on best engineering practices

Discover features designed to simplify workflows, boost productivity, and improve team collaboration seamlessly.

Discover features designed to simplify workflows, boost productivity, and improve team collaboration seamlessly.

Dashboard Image
Dashboard Image
Dashboard Image

Engineering best practices

Engineering best practices

Version control. production-like testing, proactive monitoring

Version control. production-like testing, proactive monitoring

Developer productivity

Developer productivity

Browser IDEs, CLI, automated CI/CD, centralized observability

Browser IDEs, CLI, automated CI/CD, centralized observability

Scalable compute & runtime

Scalable compute & runtime

Managed Kubernetes for Spark, dbt and Python workloads

Managed Kubernetes for Spark, dbt and Python workloads

Reliable & observable scheduling

Reliable & observable scheduling

Managed Airflow, cost-efficient scheduling, multi-environment

Managed Airflow, cost-efficient scheduling, multi-environment

Cover the full data product lifecycle

  • Data project RBAC

  • Multi-cloud

  • Multi-stack support

  • Auto-scaling compute

  • One-click rollback

  • Multi-environment support

  • Pipeline version management

  • Cost-efficient scheduling

Key Features

Key Features

Key Features

How Conveyor accelerates your data teams

Build

Schedule

Run

Deploy

Scale

Build data pipelines with the right tools

Browser IDE with zero setup or CLI for full control. Production-ready templates for Spark, dbt, and Airflow. All logs, metrics, and costs in one dashboard. RBAC built in.

Build

Schedule

Run

Deploy

Scale

Build data pipelines with the right tools

Browser IDE with zero setup or CLI for full control. Production-ready templates for Spark, dbt, and Airflow. All logs, metrics, and costs in one dashboard. RBAC built in.

Build

Schedule

Run

Deploy

Scale

Build data pipelines with the right tools

Browser IDE with zero setup or CLI for full control. Production-ready templates for Spark, dbt, and Airflow. All logs, metrics, and costs in one dashboard. RBAC built in.

Abstract
Abstract

Get started

Get started

Get started

Ready to learn more?

Ready to learn more?

Ready to learn more?


Schedule a free discovery call to discuss how Conveyor can help your team ship data products faster.


Schedule a free discovery call to discuss how Conveyor can help your team ship data products faster.


Schedule a free discovery call to discuss how Conveyor can help your team ship data products faster.

Trusted by

  • imec-logo
    ov-logo
    vito-logo
    luminus-logo

News & Insights

Read the latest on data engineering from our team

What we learn building and scaling data platforms. The good, the bad, and the OMG

What we learned building and scaling data platforms. The good, the bad and the OMG

What we learned building and scaling data platforms. the good, the bad and the OMG

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Abstract

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Product updates & the occasional data engineering truth bomb from our blog

Product updates & the occasional data engineering truth bomb from our blog