How to Build a Self-Healing Data Pipeline with Agentic AI
Overview Every data team knows the morning routine: a dbt model failed overnight, a Matillion job stalled, or a Snowflake query hit a permission error, and the dashboards
READ MOREOverview Every data team knows the morning routine: a dbt model failed overnight, a Matillion job stalled, or a Snowflake query hit a permission error, and the dashboards
READ MOREThe Announcement Santa Clara, California— CloudEQS today announced the implementation of a multi-tenant Cortex application on the Snowflake AI Data Cloud. This new application Powered by Snowflake, will
READ MOREOverview It’s no secret that AI is fueling organizations to invest in their data architecture and strategy. But what isn’t so obvious is organizations are still struggling with
READ MOREIntroduction Matillion makes it easy to build data pipelines visually, but shipping those pipelines safely across environments — dev, staging, prod — still needs the same rigor as
READ MOREOverview CloudEQS has formally joined Anthropic’s Claude Partner Network. For most firms, that would be the starting gun. For us, it’s a formal recognition of work already in
READ MOREOverview Matillion has shifted its center of gravity from Matillion ETL (METL) to Maia – its AI-native data automation platform – and every METL customer will eventually need
READ MOREOverview Enterprise data strategy has moved in waves, each one solving the last generation’s problem while quietly creating the next. First was the move to the cloud, as
READ MOREOverview As organizations modernize their data platforms, dbt (Data Build Tool) has become the standard for scalable, testable, and maintainable data transformation. However, many enterprises still rely heavily on Snowflake Views, Stored
READ MOREContext Clinical trials are the engine of modern medicine — but that engine has a serious bottleneck. Recruitment. Up to 80% of trials fail to meet their enrollment
READ MOREOverview Designing the architecture for Snowflake Cortex Analyst and Cortex Agents requires a delicate balance. When building semantic layers to empower natural language querying over your data, you quickly run into classic trade-offs
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