CloudOps & DataOps Engineer Luke Crumley describes the lightbulb moment when he fully grasped the potential of DataOps to deliver new levels of speed, innovation and quality for business teams. Data engineering is about solving problems: helping organizations to make decisions based on facts rather than guesswork. As a result, businesses need increasingly powerful tools to help them to gather, sort, manage, manipulate and present insights to support those decisions – solutions like DataOps.live.
Updates from the DataOps team and the DataOps Community
Posts about Integration:
London-based DataOps.live secures USD $10.3m Seed Funding Round with Anthos Capital and Snowflake Ventures
Latest round of investment will enable this innovative start-up to scale-up rapidly, attract even more talent, expand further into North America, and build on current project successes.
DataOps.live and Okera Joint Solution Automates and Extends Snowflake Data Security Across the Enterprise
Integrated solution ensures optimal speed and automation of Snowflake workloads while improving data governance and security with enhanced access controls.
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
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.