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Technical articles, product updates, and insights about data engineering, database optimization, and building scalable data platforms.
Learn about database relationship discovery, data cataloging, and modern data engineering practices.
Discover relationships in your data warehouse without touching production databases. Start with Snowflake or BigQuery, expand to operational sources when ready.
Opinionated guidance on dbt project structure, staging conventions, naming, testing, and incremental models based on patterns that survive real production workloads.
Production databases typically have 3-5x more implicit relationships than documented foreign keys. Where they hide and why they matter for your data team.
A practical look at Jinja compilation in dbt. What macros are, how ref() works internally, when to write your own, and when macros make things worse.
A practical guide to semantic layers for data engineers. What they actually do, when they help, and when they are unnecessary overhead.
A technical look at what semantic layers actually do under the hood. How MetricFlow and Cortex translate business questions into SQL, and what they need from your schema.
How metadata-only access works: information_schema queries, read-only users, and AES-256 encryption. No row data, no PII, no production risk.
The built-in dbt relationships test catches broken foreign keys but misses implicit ones entirely. Here is how to close the gap.
What actually matters when evaluating data catalogs: time to value, maintenance burden, and integration. Based on patterns we have seen across dozens of teams.
AI agents struggle with databases because they lack schema context. Semantic layers and automated discovery solve this. Here is the practical path forward.
Systematic approaches to uncover implicit foreign keys and undocumented relationships. Automated discovery vs manual analysis for modern data stacks.
How to discover and document relationships across multiple databases, warehouses, and sources. Practical strategies for modern multi-database architectures.
Automated vs manual data cataloging: why spreadsheets cannot keep up with modern data volumes. How to transition to automated discovery and maintenance.
Data contracts define what individual datasets promise. They rarely cover the relationships between datasets, and that gap matters.
Entity-relationship diagrams show what was documented, not what exists. The gap between the two grows over time, and most teams do not realize how wide it has become.
Not all discovered relationships are equal. Here is how multiple signals combine into a confidence score that separates real relationships from false positives.
Data lineage tracks where data flows. Relationship discovery tracks how data connects. They solve different problems, and you probably need both.
ORMs define relationships in application code that may never reach the database. Here is how Rails, Django, and SQLAlchemy each handle this differently.
Undocumented schemas cost more than anyone calculates. Here is where the time goes: onboarding, tribal knowledge, debugging, and duplication.
Foreign key constraints silently disappear during ETL. Here is what gets lost, why it matters, and what you can do about it.
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Deep dives into database optimization, relationship discovery algorithms, and data engineering best practices.
New features, improvements, and announcements about the Squish platform.
Trends in data management, AI/ML data preparation, and enterprise data strategy.
Stop spending weeks manually mapping database relationships. Discover your database relationships in 60 seconds with 95%+ accuracy.