DataOps News

Updates from the DataOps team and the DataOps Community

One-Click Data Catalogs for Snowflake

One-Click Data Catalogs for Snowflake

Our latest masterclass (held on 9 November 2021) comprised a technical session between our very own Guy Adams and Bryon Jacobs of data.world. The subject was data cataloging – gathering (or collecting) all the metadata from different systems and publishing it in a data catalog.    

#TrueDataOps & CI/CD Pipelines: Delivering Trust to Data Applications on Snowflake

#TrueDataOps & CI/CD Pipelines: Delivering Trust to Data Applications on Snowflake

The rapid and continued expansion of data systems and the exponential explosion of data drive the use cases for advanced data analytics and data science applications. However, without adopting the principles and philosophy of #TrueDataOps, it will always be challenging to develop, test, and deploy data pipelines that deliver trusted, reliable data to analytics applications and machine learning models in a short space of time and according to business stakeholder requirements.

DataOps launches support for Soda SQL and Soda Cloud

DataOps launches support for Soda SQL and Soda Cloud

With the exponentially increasing importance of and value attributed to data, it’s never been more critical to test, observe, and monitor the quality of data being used to develop many different data products, driving strategic decision-making at an organizational level. To enhance and facilitate the development of the highest-quality data products, we have recently announced our support for Soda SQL and Soda Cloud. Succinctly stated, Soda SQL has been fully integrated into our DataOps platform, with full support for Soda Cloud.

DataOps launches integration with Matillion

DataOps launches integration with Matillion

The cloud data warehouse or data cloud is increasing in importance exponentially as more organizations understand the value of using data-driven insights as a foundation for and critical part of any decision-making process. As a result, it is essential to move unstructured, structured, and semi-structured raw data from its source to a centralized location (the cloud data warehouse) to be processed, transformed, modeled, and analyzed to derive meaningful insights or information.