Recce - Trust, Verify, Ship
@datarecce.bsky.social
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Recce - Explore, validate, and share data impact before merging | Product Hunt
Recce helps data teams discover actual data impact and turn insight into actionable checklists for dbt pull request reviews. It’s a practical way to implement data best practices - know what’s changin...
www.producthunt.com
Recce 1.0 is now live on Product Hunt!
www.producthunt.com/posts/recce-4
Upvote and leave a comment to help us grow the Recce community and bring better data review processed to more data teams
Thanks for your support!
#OpenSource #Data #DataEngineering #Analytics #DeveloperTools #dbt
www.producthunt.com/posts/recce-4
Upvote and leave a comment to help us grow the Recce community and bring better data review processed to more data teams
Thanks for your support!
#OpenSource #Data #DataEngineering #Analytics #DeveloperTools #dbt
Proud and excited to be hosting so many community events today & tomorrow in San Francisco for @techweekbya16z.bsky.social!
hosting my first unconference today + happy hour tonight with @posthog.com & Deskree 🍻 for #SFTechWeek
come join us at 4pm for an ice cream truck & drinks at 5:30pm partiful.com/e/zE0NqSFH0Y...
come join us at 4pm for an ice cream truck & drinks at 5:30pm partiful.com/e/zE0NqSFH0Y...
data λ code happy hour - #SFTechWeek | Partiful
Relax after the data λ code: data unconference (https://partiful.com/e/dJSorciPRp9wUH9EcM73) with drinks, pizza, and friendly faces. Or if you couldn't make the unconference, come join to catch-up on...
partiful.com
October 7, 2025 at 5:05 PM
Proud and excited to be hosting so many community events today & tomorrow in San Francisco for @techweekbya16z.bsky.social!
Reposted by Recce - Trust, Verify, Ship
hosting 3 events in sf this week 💪
tues 2-5:30pm: data + code unconference (you set the agenda)
tues 4pm: happy hour w/ posthog + deskree (ice cream + pizza)
wed 5pm: devex demos at convex (vercel, grafana, arthur ai, deskree, convex, recce)
pure community vibes. links in thread 👇
tues 2-5:30pm: data + code unconference (you set the agenda)
tues 4pm: happy hour w/ posthog + deskree (ice cream + pizza)
wed 5pm: devex demos at convex (vercel, grafana, arthur ai, deskree, convex, recce)
pure community vibes. links in thread 👇
October 6, 2025 at 8:07 PM
hosting 3 events in sf this week 💪
tues 2-5:30pm: data + code unconference (you set the agenda)
tues 4pm: happy hour w/ posthog + deskree (ice cream + pizza)
wed 5pm: devex demos at convex (vercel, grafana, arthur ai, deskree, convex, recce)
pure community vibes. links in thread 👇
tues 2-5:30pm: data + code unconference (you set the agenda)
tues 4pm: happy hour w/ posthog + deskree (ice cream + pizza)
wed 5pm: devex demos at convex (vercel, grafana, arthur ai, deskree, convex, recce)
pure community vibes. links in thread 👇
#SFTechWeek calendar just dropped & we're doing something different at Recce.
Instead of the slide decks + swag bags that most of these become, we're hosting events focused on real community.
Thread on our lineup 👇
Instead of the slide decks + swag bags that most of these become, we're hosting events focused on real community.
Thread on our lineup 👇
September 10, 2025 at 5:35 PM
#SFTechWeek calendar just dropped & we're doing something different at Recce.
Instead of the slide decks + swag bags that most of these become, we're hosting events focused on real community.
Thread on our lineup 👇
Instead of the slide decks + swag bags that most of these become, we're hosting events focused on real community.
Thread on our lineup 👇
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
Under time constraints, data practitioners can only rely on validation scripts.
Impact Radius addresses this challenge through metadata analysis alone.
cloud.reccehq.com
Recce Cloud
Generated by Recce Cloud
cloud.reccehq.com
September 2, 2025 at 7:38 PM
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
Under time constraints, data practitioners can only rely on validation scripts.
Impact Radius addresses this challenge through metadata analysis alone.
cloud.reccehq.com
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.
❌ Random data exploration
✅ Metadata-guided investigation
Click problematic column → column lineage shows derived or passthrough → trace upstream → identify real issue.
Building Impact Radius #3: Three Essential Workflows for Data Teams
After building Impact Radius, we realized showing the tool isn't enough. You need to see HOW it fits into your daily workflow.
blog.reccehq.com
September 1, 2025 at 1:11 AM
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.
❌ Random data exploration
✅ Metadata-guided investigation
Click problematic column → column lineage shows derived or passthrough → trace upstream → identify real issue.
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
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
August 30, 2025 at 9:41 AM
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
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.
"Now, 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, viadukt
reccehq.com/case-study-v...
"Now, 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, viadukt
reccehq.com/case-study-v...
reccehq.com
August 28, 2025 at 10:08 AM
💡 For viadukt, data accuracy isn't a nice-to-have. It's core to their product.
"Now, 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, viadukt
reccehq.com/case-study-v...
"Now, 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, viadukt
reccehq.com/case-study-v...
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
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
August 25, 2025 at 9:36 PM
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
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
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.
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.
August 21, 2025 at 9:38 PM
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.
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.
"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.
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.
August 20, 2025 at 9:41 PM
"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.
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.
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-imp...
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-imp...
August 20, 2025 at 1:10 PM
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-imp...
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-imp...
🏗️ 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-v...
#DataQuality #DataValidation #TechTransformation
German renovation platform viadukt transformed their data team from reactive firefighting to proactive quality assurance.
Read reccehq.com/case-study-v...
#DataQuality #DataValidation #TechTransformation
reccehq.com
August 20, 2025 at 10:06 AM
🏗️ 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-v...
#DataQuality #DataValidation #TechTransformation
German renovation platform viadukt transformed their data team from reactive firefighting to proactive quality assurance.
Read reccehq.com/case-study-v...
#DataQuality #DataValidation #TechTransformation
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
👉Explore metadata diffing instantly at cloud dot reccehq dot com
August 19, 2025 at 9:32 PM
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
👉Explore metadata diffing instantly at cloud dot reccehq dot com
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 technical bridge from concept to working system often requires specialized expertise.
Read more about how Recce does in our blog.
August 14, 2025 at 8:01 AM
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 technical bridge from concept to working system often requires specialized expertise.
Read more about how Recce does 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
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
August 13, 2025 at 1:17 AM
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
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
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 breaking change analysis isn't enough 👇
reccehq.com/blog/Buildin...
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 👇
reccehq.com/blog/Buildin...
July 27, 2025 at 8:01 PM
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 breaking change analysis isn't enough 👇
reccehq.com/blog/Buildin...
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 👇
reccehq.com/blog/Buildin...
🚨 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/Buildin...
Software Dev: Breaking changes = planned improvements
Analytics Engineering: Breaking changes = unplanned problems
Solution: Column-level precision showing what actually needs validation.
reccehq.com/blog/Buildin...
July 25, 2025 at 7:02 PM
🚨 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/Buildin...
Software Dev: Breaking changes = planned improvements
Analytics Engineering: Breaking changes = unplanned problems
Solution: Column-level precision showing what actually needs validation.
reccehq.com/blog/Buildin...
Recce moved to reccehq.com
Previous domain redirects automatically.
Headquarters for validating, verifying, and shipping data changes with confidence.
Check it out: reccehq.com
Previous domain redirects automatically.
Headquarters for validating, verifying, and shipping data changes with confidence.
Check it out: reccehq.com
Recce - Explore, validate, and share data impact before merging
Recce helps data teams discover actual data impact and turn insight into actionable checklists for dbt pull request reviews. It's a practical way to implement data best practices - know what's…
reccehq.com
July 25, 2025 at 11:35 AM
Recce moved to reccehq.com
Previous domain redirects automatically.
Headquarters for validating, verifying, and shipping data changes with confidence.
Check it out: 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 👇
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 👇
July 23, 2025 at 8:02 PM
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 👇
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.
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.
July 23, 2025 at 2:03 PM
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.
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/Buildin...
The solution seemed simple: just combine the breaking change analysis and column-level linage, right? Wrong. 😵
reccehq.com/blog/Buildin...
Building Impact Radius #2: The Technical Breakthrough
A technical deep dive into building Impact Radius: how we combined column-level breaking change and column-level dependency analysis to answer 'What do I actually need to validate to ensure my data…
reccehq.com
July 22, 2025 at 6:02 AM
'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/Buildin...
The solution seemed simple: just combine the breaking change analysis and column-level linage, right? Wrong. 😵
reccehq.com/blog/Buildin...
**Data validation is only useful if teams adopt it.**
Early demos of Recce, which highlights exactly what changes and how to validate, showed strong response. But low setup success rates revealed a different story.
The team is now reducing this friction. Stay tuned.
Early demos of Recce, which highlights exactly what changes and how to validate, showed strong response. But low setup success rates revealed a different story.
The team is now reducing this friction. Stay tuned.
July 21, 2025 at 12:03 PM
**Data validation is only useful if teams adopt it.**
Early demos of Recce, which highlights exactly what changes and how to validate, showed strong response. But low setup success rates revealed a different story.
The team is now reducing this friction. Stay tuned.
Early demos of Recce, which highlights exactly what changes and how to validate, showed strong response. But low setup success rates revealed a different story.
The team is now reducing this friction. Stay tuned.
While building breaking change analysis and CLL, a question emerged: 'What if impact could be seen at the column level?'
Instead of 'this model has a breaking change, validate everything downstream,' teams could say 'this column changed, validate only what uses it.'
reccehq.com/blog/Buildin...
Instead of 'this model has a breaking change, validate everything downstream,' teams could say 'this column changed, validate only what uses it.'
reccehq.com/blog/Buildin...
Building Impact Radius #2: The Technical Breakthrough
A technical deep dive into building Impact Radius: how we combined column-level breaking change and column-level dependency analysis to answer 'What do I actually need to validate to ensure my data…
reccehq.com
July 20, 2025 at 4:01 AM
While building breaking change analysis and CLL, a question emerged: 'What if impact could be seen at the column level?'
Instead of 'this model has a breaking change, validate everything downstream,' teams could say 'this column changed, validate only what uses it.'
reccehq.com/blog/Buildin...
Instead of 'this model has a breaking change, validate everything downstream,' teams could say 'this column changed, validate only what uses it.'
reccehq.com/blog/Buildin...
Breaking change analysis reveals WHAT changed, but not what to DO about it.
Teams discover a model has a partial breaking change. But which downstream models need validation? Which columns?
reccehq.com/blog/Buildin...
Teams discover a model has a partial breaking change. But which downstream models need validation? Which columns?
reccehq.com/blog/Buildin...
July 18, 2025 at 6:29 AM
Breaking change analysis reveals WHAT changed, but not what to DO about it.
Teams discover a model has a partial breaking change. But which downstream models need validation? Which columns?
reccehq.com/blog/Buildin...
Teams discover a model has a partial breaking change. But which downstream models need validation? Which columns?
reccehq.com/blog/Buildin...
'Validate everything downstream' is expensive and wasteful.
Impact Radius changes that to 'validate exactly what matters' with column-level precision.
🙌 Explore the insights and discoveries that shaped this approach.
reccehq.com/blog/Buildin...
Impact Radius changes that to 'validate exactly what matters' with column-level precision.
🙌 Explore the insights and discoveries that shaped this approach.
reccehq.com/blog/Buildin...
July 15, 2025 at 4:01 AM
'Validate everything downstream' is expensive and wasteful.
Impact Radius changes that to 'validate exactly what matters' with column-level precision.
🙌 Explore the insights and discoveries that shaped this approach.
reccehq.com/blog/Buildin...
Impact Radius changes that to 'validate exactly what matters' with column-level precision.
🙌 Explore the insights and discoveries that shaped this approach.
reccehq.com/blog/Buildin...