Integration fuels innovation: data.world and Snowflake Horizon paving the way for Next-Generation Data Governance
Posted: Tue Feb 11, 2025 4:37 am
I love blogging about the exciting innovations that data.world is building on top of Snowflake. As a data catalog platform powered by Snowflake, data.world not only increases the efficiency and effectiveness of its internal data and analytics programs, but also quickly brings cutting-edge innovations to the market by leveraging Snowflake’s native governance features. Check out this YouTube video in the Powered by Snowflake series where our co-founder, Jon Loyens shares more about why we chose Snowflake as our analytics engine. In this article, I will cover what data.world provides to joint customers as a part of the Snowflake Horizon partner ecosystem.
Vision for Snowflake Horizon:
Snowflake has always been committed to providing a strong governance framework for technology partners to integrate seamlessly into its cloud platform. The latest launch of Snowflake Horizon introduces a built-in governance solution, featuring a unified set of compliance, security, privacy, interoperability, and access capabilities. Let me delve into the details of how data.world, the leading data catalog and governance platform, is leveraging these native features to france whatsapp number data provide our customers with advanced Data Governance capabilities.
Compliance: Extending Snowflake Data Metric Functions to everyday workflows
Last week, we announced our native integration of Snowflake Data Metric Functions (DMFs), which is currently in Private Preview. Customers can create their own metrics by writing custom SQL to monitor the quality of their data. For organizations seeking maximum flexibility in building their own Data Quality monitoring solution, this announcement is particularly exciting.
While measuring the quality of your data is crucial, it is equally important to share and distribute data quality information to relevant stakeholders effectively. The integration to data.world accomplishes this objective by automatically delivering data quality status and metrics to the people who need it, when and where they need it. By doing so, we streamline communications between data teams to drive better data quality. In the diagram below, you can see how data.world’s Hoot, an embeddable trust signal, is used to 1) detect data health issues from these Snowflake metric functions upstream, and 2) broadcast the results in downstream analytics applications.
Vision for Snowflake Horizon:
Snowflake has always been committed to providing a strong governance framework for technology partners to integrate seamlessly into its cloud platform. The latest launch of Snowflake Horizon introduces a built-in governance solution, featuring a unified set of compliance, security, privacy, interoperability, and access capabilities. Let me delve into the details of how data.world, the leading data catalog and governance platform, is leveraging these native features to france whatsapp number data provide our customers with advanced Data Governance capabilities.
Compliance: Extending Snowflake Data Metric Functions to everyday workflows
Last week, we announced our native integration of Snowflake Data Metric Functions (DMFs), which is currently in Private Preview. Customers can create their own metrics by writing custom SQL to monitor the quality of their data. For organizations seeking maximum flexibility in building their own Data Quality monitoring solution, this announcement is particularly exciting.
While measuring the quality of your data is crucial, it is equally important to share and distribute data quality information to relevant stakeholders effectively. The integration to data.world accomplishes this objective by automatically delivering data quality status and metrics to the people who need it, when and where they need it. By doing so, we streamline communications between data teams to drive better data quality. In the diagram below, you can see how data.world’s Hoot, an embeddable trust signal, is used to 1) detect data health issues from these Snowflake metric functions upstream, and 2) broadcast the results in downstream analytics applications.