How to Generate a Time Spine in dbt
A time spine is a table with one row per time period used for filling gaps in event data. Learn how to generate a time spine in dbt using date_spine, generate_series, and MetricFlow conventions.
A time spine is a table with one row per time period used for filling gaps in event data. Learn how to generate a time spine in dbt using date_spine, generate_series, and MetricFlow conventions.
An AI data review agent automates dbt PR review by analyzing code changes, running data validations, and generating impact summaries. Learn how multi-agent architecture produces trustworthy reviews.
The dbt DAG is a directed acyclic graph that maps dependencies between your data models. Learn how to read the DAG, use lineage for impact analysis, and understand the difference between static and diff-aware lineage views.
A comparison of Recce and Datafold for dbt data validation. Covers validation philosophy, CI/CD integration, pricing, and when to choose each tool.
A structured guide to implementing data review processes that catch data quality issues before they reach production. Covers impact analysis, automated checks, and CI/CD integration for dbt projects.
Impact radius measures how far a data model change propagates through your DAG. Learn how to calculate, visualize, and use impact radius to scope data reviews and reduce production risk.
dbt tests check structure, not data impact. Learn what additional checks — schema diffs, row counts, profile diffs, and automated preset checks — your CI pipeline should run to catch issues before merging.
A structured guide to writing dbt pull requests that include data validation, not just code changes. Covers PR templates, data impact documentation, and review workflows.
dbt tests validate structure, not meaning. Learn why data can pass all tests and still be wrong, and what practices catch the semantic errors that automated testing misses.
Column-level lineage tracks how individual columns flow through your data pipeline. Learn how CLL works, its three core use cases, and how it compares across dbt ecosystem tools.
A data diff compares datasets across two environments to surface what changed. Learn the types of data diffs, when each is useful, and how to avoid the hidden costs of diff-everything approaches.