Five stories to understand
01 · Modern Data Stack
AWS’s agentic lakehouse: data access and governance
AWS’s multi-cloud lakehouse pattern puts catalogue access and permissions at the centre of agent design.
Read analysis →02 · Cloud Platforms
AWS and Salesforce: governing zero-copy data access
AWS describes direct access from Salesforce Data 360 to Iceberg data in S3. Shared access still needs explicit ownership.
Read analysis →03 · BI & Analytics
BigQuery AI.AGG: summarisation, quality and cost
AI.AGG brings grouped text and image summarisation into BigQuery. Test the summaries as carefully as the query.
Read analysis →04 · Data Governance
Gemini Enterprise logs: what data teams should monitor
Google’s BigQuery logging pattern supports deeper analysis of Gemini Enterprise use. The telemetry also needs its own controls.
Read analysis →05 · Data Engineering
Snowflake Cortex AI Function Studio: quality and cost
Snowflake’s AI Function Studio evaluates quality and inference cost together. A vendor benchmark is a starting point for your own test.
Read analysis →
Three tools to evaluate
- BigQuery AI.AGG
Evaluate summaries against the source records, including unusual cases.
- Snowflake Dynamic Tables
Check how partial updates and intentional nulls affect current state.
- Maia Foundation
Compare generated pipelines with trusted outputs and record review effort.