Resource Library
Enterprise guides for data governance, quality, architecture, and AI readiness.
Use these resources to frame the decisions behind a modern data program: which datasets matter, how trust is measured, where ownership lives, and what needs to be true before analytics and AI can scale safely.
The guides are written for data leaders, platform teams, governance owners, and technical executives who need clear direction without vendor noise.
Start with a current initiative, then work backward to the controls it needs. A reporting modernization effort may need semantic definitions and dbt tests. An AI initiative may need documented lineage and quality thresholds. A telemetry program may need streaming architecture and observability from the beginning.
Pillar Guides
Start with the architecture or operating question in front of you.
Platform Strategy
Modern Data Platforms and Governance
How architecture, ownership, lineage, quality, and analytics delivery fit together.
AI Readiness
AI-Ready Enterprise Data
Prepare data foundations before AI systems rely on business context.
Warehouse Quality
Snowflake, dbt, and Data Quality
Modeling, tests, documentation, lineage, and trusted analytics workflows.
High-Volume Data
Satellite and IoT Data Architecture
Streaming, telemetry, geospatial data, governance, and observability patterns.
Support Guides