A data review flagged 99.999% row-count variance. The PR was two lines.

Base was five years of production history. Current was a one-hour CI build. Neither was wrong. They were built for different jobs.

False alarms like this train reviewers to scroll past real variance. That is the damage.

https://blog.reccehq.com/session-base-per-pr-why-data-reviews-lie
#dbt #DataEngineering #AnalyticsEngineering

Auto-diff every model on every PR? Tempting.
But you’ll get ⚠️ dozens of alerts, most irrelevant.

CI without context = alert spam.

Real-world data work needs more than diffs: what changed, why, and what to do.

Human judgment matters.
Recce helps automate with opinions.

👉 https://datarecce.io/blog/more-than-data-diff/

#dataengineering #datadiff #analyticsengineering #datavalidation

Data diff tells you something changed. but NOT:

❓ Was it expected?
⛏️ Worth investigating?
🔗 What depends on it?

Let’s stop mistaking output for insight.
See how 👉 https://datarecce.io/blog/more-than-data-diff/

#dataengineering #datadiff #analyticsengineering #datavalidation

"The value didn't justify the effort."
A Sr. Data Engineer at Swedish MediaTech after their Datafold PoC.

❌ Heavy setup → Noisy results → Alert fatigue → No way to start small
Comparison: https://datarecce.io/blog/recce-vs-datafold/

#DataEngineering #DataValidation #analyticsengineering

Don’t start with what changed. Start with what SHOULD change!

Because not every diff is a problem, and not every problem shows up as a diff.

👉 https://datarecce.io/blog/more-than-data-diff/

#dataengineering #datadiff #analyticsengineering #datavalidation

Want to better understand how your data models work, and what might break when they change?

Here are 5 types of column transformations in #dbt models:
1. Pass-through
2. Renamed
3. Derived
4. Source
5. Unknown

Each one helps you assess impact and trace data through your pipeline more clearly

We use these types in Recce to power column-level lineage and breaking change analysis

Read the deep dive
https://datarecce.io/blog/column-level-lineage-internals/

#SQL #Data #OpenSource #DataEngineering #AnalyticsEngineering

Stop duplicating dashboards to preview the impact of dbt model changes!

With Recce, you can:
✅ Diff models
✅ Record data impact
✅ Share results instantly — no dashboards, no SQL, no screenshots

One click. Instant clarity:

https://medium.com/inthepipeline/stop-duplicating-dashboards-just-to-preview-dbt-changes-21ff0ba8dd21

#dbt #DataOps #SQL #data #analyticsengineering #DataEngineering

GenAI just got real for analytics engineers. See how dbt Copilot is changing workflows in our latest blog👇

https://dataroots.ghost.io/genai-is-taking-over-the-modern-data-stack-and-dbt-copilot-is-leading-the-way-in-analytics-engineering-2/

#GenAI #dbt #AnalyticsEngineering #DataStack

Column-level lineage is now available in Recce 0.57

Add it to your dbt data validation workflow:

1. Lineage Diff - Focus on impacted models
2. Breaking Change Analysis - Eliminate irrelevant changes
3. Column-Level Lineage - Track column evolution

#dbt #DataEngineering #Data #SQL #Analytics #AnalyticsEngineering

❌ No more manually cross-referencing dbt docs from dev and prod

❌ No more manually checking schemas in your data warehouse

❌ No more manually comparing row-counts on models.

See a trend?

Read how an experienced data professional validates zero regression on a #dbt PR:

https://www.linkedin.com/posts/abdelm_recce-dbt-activity-7300436694808358914-aKiF?rcm=ACoAAAVLBgkB6r61wCsOcKgFDrtf_EEEe3UjdXs

#DataEngineering #dbt #Data #Analytics #AnalyticsEngineering #SQL #BigQuery #OpenSource