Yelp outlines its approach to processing Amazon S3 server-access logs at scale, addressing challenges like high log volume and storage costs. They now compress logs into Parquet files, greatly reducing storage needs and improving query performance for analytics tasks. This system supports various operational use cases, from debugging to cost analysis.

This article outlines the importance of having governed and discoverable data for successful AI projects. It highlights common pitfalls in AI implementation and presents a structured approach to ensure data quality and compliance. A roadmap is provided for creating a reliable data stack that supports effective AI systems.

This article argues that data teams should transition to context engineering, integrating data governance, engineering, and science to create reliable knowledge sources for AI agents. It highlights the need for a structured context stack to ensure accurate answers and effective performance from these agents.

Discrepancies in reported monthly active user counts among different teams stemmed from varying definitions and interpretations of what constitutes an "active user." After a thorough audit, a unified definition was established and implemented consistently, leading to more productive leadership meetings focused on actionable insights rather than resolving conflicting data reports.