How does a premier data product company translate abstract concepts into production reality? Explore the concrete methodology DataKnobs uses to architect, build, and scale data assets.
DataKnobs doesn't treat data as bespoke, handcrafted projects. They treat it like a manufacturing pipeline with strict quality control, standardized interfaces, and clear ownership.
Before a single line of ETL code is written, DataKnobs establishes a strict YAML-based data contract between the producers and consumers.
DataKnobs never shares raw database access. They expose their data exclusively through standardized "Output Ports" optimized for the user's tools.
A data product doesn't exist at DataKnobs unless it's in the central catalog. Registration is an automated part of the CI/CD deployment pipeline.
DataKnobs treats every data product as an independent, deployable architectural quantum. This means the code, the data, and the infrastructure are version-controlled and deployed together.
DataKnobs doesn't use a central data team. They use embedded pods consisting of a Data Product Manager, a Data Engineer, and a Domain Expert to ensure maximum business alignment.
dbt models, Spark jobs, and Airflow orchestrations.
Terraform scripts provisioning Snowflake warehouses and S3 buckets.
Great Expectations rules and automated SLA alerting.
Ready to stop building brittle pipelines and start building robust, scalable data products? Use our playbook based on industry leaders.