Recce - Making Data Productive. @DataRecce

Marketing reports conversion issues. Investigation approach matters: ❌ Random data exploration ✅ Metadata-guided investigation Click problematic column → column lineage shows derived or passthrough → trace upstream → identify real issue. blog.reccehq.com/building-impac…

Ad-hoc validation scripts accumulate from past incidents but don't transfer to new contexts. Under time constraints, data practitioners can only rely on validation scripts. Impact Radius addresses this challenge through metadata analysis alone. cloud.reccehq.com

Metadata analysis eliminates unnecessary validation queries. Data practitioners commonly validate dbt changes by checking row counts across all downstream models: 47 models generating significant warehouse costs to identify the 3 that actually changed. Try using metadata only

💡 For viadukt, data accuracy isn't a nice-to-have. It's core to their product. "with Recce Cloud, we've dramatically improved our ability to deliver reliable data and address issues before they impact our customers." Pascal Biesenbach, CEO & Co-founder reccehq.com/case-study-via…

Column-level lineage emerges from standard dbt artifacts. Running `dbt run` and `dbt docs generate` produces artifacts that enable column-level lineage visualization and impact analysis. cloud.reccehq.com accepts dbt artifacts to demonstrate metadata analysis

🙅 Stop jumping straight to expensive data diffs! Metadata-guided validation targets what actually matters, eliminating wasted time and resources. Article: blog.reccehq.com/building-impac… Try it now: cloud.reccehq.com

Data teams consistently ask: "What validation is actually needed to ensure data accuracy?" Product demos only do so much, teams need clarity on workflow integrations. In our latest blog, Karen breaks down an entire workflow with a real-world example. blog.reccehq.com/building-impac…

🏗️ How viadukt Built Trust at Scale: From Manual Data Checks to Systematic Validation German renovation platform viadukt transformed their data team from reactive firefighting to proactive quality assurance. Read reccehq.com/case-study-via… #DataQuality #DataValidation

Ccomprehensive data diffing isn't universally necessary. Resource-intensive validation should be targeted and intentional. 👉Explore metadata diffing instantly at cloud dot reccehq dot com

"The PRs created by John are always high quality. I can review them easily." Users love having data validation included in their PR process. But how easy a tool is to set up determines actual usage. Read more in our blog.

Structural changes reveal downstream risks before queries execute. This metadata-first approach transforms validation from comprehensive data testing to targeted analysis of high-risk areas. 👉Explore metadata diffing instantly at cloud dot reccehq dot com

The validation need is universal. The setup capability varies significantly. Teams with robust infrastructure can implement comprehensive validation processes. Teams without DevOps have troubles. The gap creates an adoption barrier. Read more on closing this gap in our blog.

Reading about "dbt artifacts" and "environment setup" doesn't automatically provide the infrastructure knowledge required for implementation. The technical bridge from concept to working system often requires specialized expertise. Read more about how Recce does in our blog.

The validation need is universal. The setup capability varies significantly. Teams with robust infrastructure can implement comprehensive validation processes. Teams without DevOps have troubles. The gap creates an adoption barrier. Read more on closing this gap in our blog.

A partial breaking change can have no impact on downstream models. Though breaking change analysis works at the column level, identifying a partial breaking change still isn't enough to get the precise impact radius. Why it's not enough reccehq.com/blog/Building-…

🚨 Breaking Change Analysis Gap Confusing Data Teams Software Dev: Breaking changes = planned improvements Analytics Engineering: Breaking changes = unplanned problems Solution: Column-level precision showing what actually needs validation. reccehq.com/blog/Building-…

Recce moved to reccehq.com Previous domain redirects automatically. Headquarters for validating, verifying, and shipping data changes with confidence. Check it out: reccehq.com

A partial breaking change can have full impact on downstream models. Though breaking change analysis works at the column level, identifying a partial breaking change still isn't enough to get the precise impact radius. Why breaking change analysis isn't enough 👇

Setup complexity creates an adoption barrier. Recce generates strong interest during demos, but implementation reveals a disconnect. Asking data teams to navigate DevOps processes creates friction. Read more about how Recce is addressing this in our blog.

'If breaking change analysis works at the column level, could impact radius be narrowed to the column level too?' The solution seemed simple: just combine the breaking change analysis and column-level linage, right? Wrong. 😵 reccehq.com/blog/Building-…