Senior data engineers carry validation knowledge in their heads. Learn how to capture ad-hoc checks as reusable preset validations that run automatically on every dbt PR, turning tribal knowledge into team-wide institutional knowledge.
Data Renegades is a podcast where engineers behind tools like Apache Airflow, Django, Datasette, and Apache Flink share unfiltered stories about building the data tools teams use every day.
Five real-world data problems — from AI agent benchmarking to DuckDB reconciliation to dbt cleanup — tackled live during the Data Valentine Challenge, with practical fixes for each.
A practical framework for catching semantic data failures that pass all tests — covering why data tests miss business logic errors and how to validate data correctness before production.
AI agents automate dbt data reviews using multi-agent architecture, MCP-only tool access, and structured prompts. Learn the reliability patterns that make automated PR summaries trustworthy.
Recce evolved from a single prompt to a multi-agent AI system for dbt data reviews. Learn the architectural iterations, token limit challenges, and engineering decisions behind production-grade AI data review.
Complex CI/CD requirements block data teams from adopting validation tools. Learn how sessions architecture and metadata separation eliminated 10+ minutes of setup per validation and unlocked shift-left data validation.
Recce is at Coalesce 2025 in Las Vegas demonstrating Recce Cloud, AI-powered data review, and hosting the Data Renegade Happy Hour for the data engineering community.
Learn three daily workflows where impact radius transforms data validation: root cause discovery, developer validation, and data PR review. See how metadata-first analysis replaces expensive blanket data diffing.
German energy platform vaidukt reduced customer data complaints by 70% using Recce for systematic PR-level data validation, transforming how a 3-person data team catches errors before production.
A firsthand account of letting Claude Code build an analytics warehouse end-to-end with dbt. The interesting part was not the generated code — it was the setup, review, and guardrails that made the output usable.
Guided data review uses context engineering to tell dbt PR reviewers what changed, why it matters, and what to validate. Learn how it solves the "where do I start?" problem in data PR reviews.
Data teams often love a tool in demos but abandon it weeks later. Learn why setup complexity, wrong adoption sequences, and cognitive load create adoption barriers, and how value-first design solves them.
dbt MCP handles building and testing models but cannot compare branch output against production. Learn why MCP-based dbt workflows need a dedicated validation layer for cross-environment data diffs.
A practical account of using Claude Code to build an end-to-end dbt analytics warehouse, from Snowflake ingestion to mart models, and why the setup infrastructure matters more than the prompt.
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.