Fintel Analytics blog
Expert perspectives on data analytics, AI agents, internal tools, and business intelligence from the Fintel Analytics team.
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Using dbt run-operation or BigQuery stored procedures for transformations hides logic from your DAG, breaks lineage, and silently corrupts downstream models. Here is how to find and fix it.
Hardcoded table names in dbt BigQuery models silently break your DAG, making downstream failures invisible until something stops working in production.
Undocumented dbt dependencies are silently breaking production dashboards at growth-stage companies. Here is how to audit your DAG and fix it before it costs you.
dbt models can silently scan BigQuery tables that never appear in your DAG. Here's how to find them in under five minutes using INFORMATION_SCHEMA.JOBS.
Choosing between dbt macros and persistent SQL UDFs in BigQuery is a decision most teams get wrong — and the cost shows up in your bill and your lineage graph.
Running a single dbt project works — until it doesn't. Learn when to split into multiple projects in BigQuery, how to do it without breaking lineage, and the hidden costs teams miss.
dbt source fan-out silently duplicates BigQuery scans, breaks lineage, and strips your pipeline of test coverage. Here is how to find and fix it.
dbt script models using CREATE TEMP TABLE in BigQuery silently break lineage and inflate warehouse costs. Here is how to find and fix them.
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