8 articles found
ML teams are discovering that high-performance embedding formats like Lance create severe discoverability and governance issues when scattered across S3, exposing a critical gap in MLOps tooling.
AI-generated code has resurrected semantic drift at scale, forcing a return to intentional data modeling. Here’s why the math leaves no alternative.
A critical vulnerability exposes how Snowflake’s row-level security policies can be completely bypassed using Python UDFs, putting your most sensitive data at risk.
Why traditional data governance creates bureaucracy bottlenecks and how active, federated models embed compliance directly into daily workflows.
93% of executives use unapproved AI tools, higher than any other employee group, creating massive data leakage risks while writing AI policies they ignore.
Why engineering teams are tired of cleaning up upstream messes that product managers should own from day one.
Semantic layers, once considered legacy, are experiencing renewed interest due to the need for standardized, AI-readable data definitions across BI and analytics platforms.
As Databricks’ Unity Catalog goes all-in, companies face a stark choice: embrace vendor lock-in or build fragile federated systems.
