‘Let me check row counts on all 47 downstream models' every data practitioner validating dbt changes.
That's $$ in warehouse costs just to find the 3 models that actually changed.
Metadata shows you those 3 models instantly, before you run a single query.
When you have limited time to make changes, the realistic way to balance quality is running ad-hoc queries 🤞. But when you work on new models or join a new company, you start over building your validation from scratch.
Impact Radius solves this problem with metadata alone.
You can run `dbt run` and then `dbt docs generate` to view the lineage as shown in the first screenshot. With the artifacts generated by these two commands, you can actually view column-level lineage and visualize changes and their impact at the column level.
See exactly which dbt models your changes will break in 2 minutes, without any setup.
Last week, we launched 👋 https://t.co/xlgX5Unjio for users to experience Recce more easily by uploading dbt artifacts or using our sample artifacts.
🤔 Data teams keep asking: "What do I actually need to validate to ensure my data is right?"
See real example, real workflow, real time and cost savings. 💰
https://t.co/KGKUUtsdpf
Every data transformation begins with metadata modifications: column additions, type changes, new dependencies, or altered join logic.
Can we use this metadata to find potential issues before running costly queries 💸?
👉Explore metadata diffing https://t.co/0eVWceZlYP
"The PRs created by John are always high quality. I can review them easily."
Our most successful users catch issues before submitting PRs. But getting there is painful 🙈
Most people give up. 😭
More on our fix coming soon 👇
"I love the concept, but I can't get it working with my data."
Reading about "dbt artifacts" and "environment setup" doesn't magically give you infrastructure knowledge.
The most motivated users were getting the most frustrated. 😤
More on what we learned coming soon 👇
Teams like Prefeitura do Rio de Janeiro crushed our setup process.
https://t.co/jJhuwkgPp0
The other 80% of sign-ups? They just want to validate changes before merging PRs and may not have engineering experience.
More on how we're fixing this 👇
After our OSDE East 2025 talk, our booth was swarmed. Everyone wanted to see the lineage diff we'd demoed. People were genuinely 🤩 excited.
Then setup conversations: "I need to ask other team." 🙄
Join our journey to fix this.
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 👇
https://t.co/WlDKeb1ZU6
A partial breaking change can have full impact on downstream models.
Though we do breaking change analysis to the column level, so we know it’s a partial breaking change. It’s still not enough to get the precise impact radius.
Why👇
https://t.co/WlDKeb1ZU6
'If we can do breaking change analysis at the column level, could we narrow the impact radius to the column level too?’
Simple. Just combine the two tools, right? Wrong. 😵
🧵Form validate everything downstream → validate only what uses it.
https://t.co/WlDKeb1ZU6
'What if we could see impact at the column level?'
While building breaking change analysis and column-level lineage, we couldn't help but think this question.
This simple thought changed everything and lead us to Impact Radius 👇
https://t.co/WlDKeb1ZU6
We built an amazing data validation tool. Users loved the demo. But 80% didn't finish setting it up. 😭
The setup requires technical knowledge that data engineers handle, not the day-to-day analysts who need validation most.
We're fixing this! Join our journey 👇
Breaking change analysis tells you WHAT changed, but not what to DO.
You know a model has a partial breaking change. Great. But which downstream models need validation? Which columns?
A partial breaking change can have full impact on downstream models.
https://t.co/WlDKeb1ZU6
'Validate everything downstream' is expensive and wasteful.
Impact Radius changes that to 'validate exactly what matters' with column-level precision.
This journey took us months of research, dead ends, and breakthroughs.
🙌 Join us: https://t.co/WlDKeb1ZU6
"What do I actually need to validate to ensure my data is right?"Every data engineer asks before merging a PR.
See how we broke it down into two parts:
1️⃣ WHERE to validate? (Which actually matter?)
2️⃣ HOW to validate? (What checks are enough?)
https://t.co/ntotTvFoGW
From “validate everything” to “validate exactly what matters” took us months of research, dead ends, and breakthroughs.
We finally launched Impact Radius to help you analyze changes and s downstream impacts at the column level.
🎒 See our journey https://t.co/WlDKeb1ZU6