We sat down with Max Beauchemin, creator of Airflow and Superset. Here are the takeaways: 🧵 #DataEngineering #Airflow

I'm in an episode of the new Data Renegades podcast with CL Kao and Dori Wilson talking about data journalism and Datasette and related topics - it's a fun conversation, here are some extracted highlights simonwillison.net/2025/Nov/26/...

The data industry is noisy. We wanted to help create some quiet. 🤫 We are proud to launch the Data Renegades podcast today, the brainchild of our founder @clkao.bsky.social and Head of Data @doriwilson.com They set out to build the show they actually wanted to listen to: 🧵👇

Proud and excited to be hosting so many community events today & tomorrow in San Francisco for @techweekbya16z.bsky.social!

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 👇

#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 👇

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

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.

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. "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

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.

"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.

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...

🏗️ 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

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

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.

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

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...

🚨 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...

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/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.

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...

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...

'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...

We needed to reduce data diffing queries from the start. 20+ downstream models "impacted" by your change? You're stuck with two bad choices: 😵 Validate everything (manual hell) 🙈 Guess which ones matter (risky gamble) That's why Impact Radius helps reccehq.com/blog/Buildin...

From validating all downstream models to validating only what matters. Impact Radius delivers precision in data validation. Instead of 'validate everything downstream,' teams can now 'validate exactly this.' 🧵 How column-level precision transforms validation workflows reccehq.com/blog/Buildin...

Learn how to define and analyze the impact radius of data model changes — Dave Flynn presents a clear, hands-on walkthrough of a change-aware data-validation workflow. towardsdatascience.com/change-aware...

How do you review data changes in PRs? 📊 A) Auto-diff everything B) Explore impact then validate C) Manual spot checks D) No review We're seeing a big shift from A → B, see datarecce.io/blog/recce-v... But curious what's working or not #DataEngineering #DataValidation #dbt

Recce is now SOC 2 Type 2 compliant! Our security controls don't just work in theory, they work consistently in practice. For data teams: same speed and visibility, now with battle-tested security. Our Trust Center: trust.reccehq.com #SOC2 #DataSecurity #DataEngineering

"Which downstream models actually need my attention?" Every data engineer asks at 11pm before a deploy. Syntax and tests? Those aren't your real worry. We're obsessed with helping teams validate precisely what matters. 🧵 See how we build Impact Radius reccehq.com/blog/Buildin...

Benefits of "automate everything" data validation , but hidden cost 💸 1️⃣ Compute Spend 2️⃣ Alert Fatigue 3️⃣ Team Trust Compare automation-first vs human-in-the-loop: datarecce.io/blog/recce-v... #DataEngineering #DataValidation #dbt #DataCosts

Is high-quality data the same as correct data? Data can pass every test, but still be wrong! ✅ Schema checks ✅ Null constraints 🚫 No correctness validation Recce introduces a workflow built around data correctness Find and fix silent errors: reccehq.com/blog/high-qu... #dataquality #dbt

Your team’s judgement should become team knowledge, not just lost in Slack. Every PR review is a decision: ✅ What matters ❌ What doesn’t 🧠 Why a change is okay or not Learn how👉 datarecce.io/blog/more-th...

Choose Recce and Datafold? Datafold if: → large-scale data → automated CI/CD coverage all Recce if: → focus on dev-time validation → prefer lightweight, open-source flexibility Full comparison: datarecce.io/blog/recce-v... #DataEngineering #DataValidation #dbt #BuyersGuide

Auto-diff every model on every PR? You’ll get ⚠️ dozens of alerts, irrelevant! CI without context = alert spam. Real-world data work needs: what changed, why, and what to do. Learn how 👉 datarecce.io/blog/more-th... #dataengineering #datadiff #analyticsengineering #datavalidation

Hot take: Automating ALL data diffs by default is backwards 🔥 🤖 Datafold's automation-first vs 🙋Recce's human-in-the-loop philosophy Getting 50 automated alerts or 5 targeted insights? See comparison datarecce.io/blog/recce-v... #DataEngineering #DataValidation #datadiff

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 👉 datarecce.io/blog/more-th... #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 Comparsion: datarecce.io/blog/recce-v... #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. 👉 datarecce.io/blog/more-th... #dataengineering #datadiff #analyticsengineering #datavalidation

🚨 Data diff isn’t enough. You’re putting out harmless fires while real metric failures burn unnoticed. You don’t need more diffing. You need better understanding. Read 👉 datarecce.io/blog/more-th... #dataops #datadiff #datavalidation #dataengineering

Understanding how columns are transformed in a dbt model is critical for impact analysis, QA, and trust in your pipeline Here are the 5 column transformation types you should know... 🧵👇 #dbt #dataengineering #analyticsengineering

What breaks if I change this column? Read our technical deep-dive into how Recce constructs column-level lineage from #dbt models - Column origins & transformations - Column classification - Tricky edge cases Read more: datarecce.io/blog/column-... #data #lineage #dataops #dataquality #SQLGlot

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. medium.com/inthepipelin... #dbt #DataOps #SQL #data #analyticsengineering

Column-level lineages have been getting a lot of buzz in data-land, love seeing how Recce is not just visualizing the flow, but allowing people to analyze data from there.

Thanks to all how joined us for the Data Renegades happy hour during the #DataCouncil “No BS Data Conf” this week Did you try a "Look Before Merging" cocktail? We were joined by the best co-hosts ❤️ @tobikodata.com @bauplan.bsky.social @datacoves.com @typedef.ai See you all next year! #NoBSConf

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