What methods does DataKnobs employ to turn theoretical ideas into tangible products? Delve into the specific strategies the premier data product company utilizes to design, construct, and expand data resources.
DataKnobs does not approach data as unique, artisanal creations. Instead, they view it as a production line with stringent quality standards, standardized connections, and defined ownership.
DataKnobs creates a rigorous YAML-based data contract between producers and consumers prior to writing any ETL code.
DataKnobs always ensures that raw database access is never shared. Instead, they make sure to only expose their data through standardized 'Output Ports' that are optimized for the user's tools.
At DataKnobs, a data product must be in the central catalog to exist, and registration is automatically included in the CI/CD deployment process.
DataKnobs considers each data product as a self-contained architectural unit that includes code, data, and infrastructure, all version-controlled and deployed together.
DataKnobs does not rely on a central data team. Instead, they utilize embedded pods comprised of a Data Product Manager, a Data Engineer, and a Domain Expert to achieve optimal business alignment.
dbt models, Spark jobs, and Airflow orchestrations.
Terraform scripts provisioning Snowflake warehouses and S3 buckets.
Great Expectations rules and automated SLA alerting.
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