Data
insights
Filtered field notes for Data work, newest first.
Turning Manual Data Checks into Maintainable Automated Rules
A practical guide to extracting human judgment, designing rule contracts, and deploying automated data checks with safe failure handling.
Designing a Trusted Data Layer for Reports and AI Q&A
A practical architecture for giving dashboards and AI assistants consistent, traceable, and governed access to enterprise data.
Monitoring Schema Drift in Data Pipelines: Contracts, Detection, and Safe Recovery
A practical framework for detecting schema drift, assessing downstream impact, and recovering pipelines without losing data.
Turning Unstructured Documents into a Searchable Knowledge Base
A practical guide to document ingestion, extraction, chunking, hybrid retrieval, access control, and ongoing knowledge-base quality.
Real-time Analytics Pipelines: A Kafka Primer and Trade-offs
A practical guide to when Kafka helps, where it adds complexity, and how to design real-time analytics pipelines responsibly.
Why Data Governance Is a Prerequisite for AI Adoption
Data governance is not paperwork; it is the engineering foundation that lets enterprise AI use data safely and reliably.
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