u/SignalForge007

well I myself have experience this , a PR looks harmless , everything passes dbt tests ,CL ,deployment , then days later somebody realise

• executive KPIs drifted
• finance dashboards became inconsistent
• forecasting outputs changed
• downstream reports silently broke

what I am wondering is
would a ightweight GitHub/dbt PR tool that warns BEFORE merge about likely downstream operational/business consequences be valuable enough to be paid ?
not 12 downstream models affected but This change may destabilize revenue trend consistency and affect forecasting dashboards
The idea would be:

• GitHub App / CI integration
• ~30 second setup
• automatic PR comments
• low-noise / high-confidence warnings
• focused on semantic/business impact, not generic alerts

is this a painful workflow gap or minor incovinience?
would love brutally honest opinions from you guys

I have seen cases when in tests the mocel was behaving very nicely 95% accuracy but then weeks later, business KPIs start drifting and someone eventually realizes the model quality had been degrading the entire time.
how do you guys trace these issues and when you know about them how do you fix them ? is it a small 10 sec process to fix these issues or a longer one ,

I have seen cases about production ML failures and I keep seeing this Model trains at 87% accuracy,Deploys fine, no errors in logs, API returns 200s , Predictions look reasonable Everything seems healthy
then 2 to 3 weeks later , buisness metric starts to drop quitely and surprisingly no one notices until someone manually digs into the data and realizes the model has been degrading the whole time.
I am curious about how you guys handle this in practice and how much time is wasted in catching these issues

my team literally spend hours debugging just why and what broke in the pipline , so i built a built a tool which helped me a lot it basically tells why and what broke in your data pipline it has the following functions right now

probable root cause

related SQL changes

downstream blast radius

confidence scoring

suggested investigation direction

ingestion failure tracking

https://preview.redd.it/hhgw2f4p1jzg1.png?width=1920&format=png&auto=webp&s=33e2ca3de1177bd04e6af34b2bf4466dc4c596bc

https://preview.redd.it/y1whgbqm1jzg1.png?width=1920&format=png&auto=webp&s=c1935fb6d1a1df960014d2fa45c4a38ff594d64d

I was wondering if it was just me who needed this or is this problem widespread , to me it made time for debugging from 2 to 3 hr to straight 5 to 10 minutes

most data tools catch anamolies but nothing tells
what actually broke
what was the reason ? is that sql logic change , ingestion issue or scheme issue or what
so I made my own tool for this so
instead of just row count change
the system tries to produce
→ probable root cause
→ related SQL changes
→ downstream blast radius
→ confidence score
→ operational context
→ suggested investigation path
example from my current prototype
raw_orders suddenly jumped to 20.2% NULLs
→ downstream joins impacted
→ correlated with related upstream signals
→ severity + trust score assigned
→ likely RCA surfaced automatically
but more I work on this , the more bottleneck i find in modern data stacks
my team literally spent 2 to 3 hours just figuring out what caused this , especially with new comers
I was curious if it was only me who faced this or is it widespread
and does it look useful ?

https://preview.redd.it/6bdvb57ezizg1.png?width=1920&format=png&auto=webp&s=3b7b1f7471d69d6fdc0604e720c365aa7ea82908

https://preview.redd.it/ublrmosfzizg1.png?width=1920&format=png&auto=webp&s=3e63932a6d8b76a2075e6b36bb0fb098a616333b

this is the current state of the prototype

I see this a lot whenever I'm working on data pipelines.

The dashboard will inexplicably fall by 30 to 50%, and everyone panics and spends a lot of time trying to figure out what is going on.

We need to figure out if it was the SQL query, or something in the workflow, and find out how many of our other dashboards are affected as well.(took a lot of time for me to do that )

This involves checking all aspects of the workflow most of the time to identify the root cause of the problem.

I attempted to build a tool to fix this

The tool was meant to achieve a couple of objectives:

• Identify anomalies-for instance, sudden decreases in dashboard metrics.
• Track data lineage to identify data sources.
• Pinpoint likely causes of the problem-such as a modified SQL filter.
• Outline all affected models within the workflow.

In one instance, a change from "id is less than one hundred" to "id is less than twenty" triggered a significant drop in the dashboard.

The tool was effective in diagnosing the issue, tracing it back to the change in the code, and validating the fix.

I am questioning whether my current approach is an overcomplication of the issue.

How do you handle the problem of this kind in day to day activities and will something like this save time or is it just fluff?

Is this an area that truly needs a solution, or do current methods effectively resolve the issue?

when a problem occurs where like
a dashboard drops by 40%
leading to panic , chaos then spend an hour or two finding
which model broke it
whether its sql or upstream
so to counter this
I built a system which detects anomalies
traces lineage
identify root cause and
explain whether sql change caused the problem
and shows downstream impact
Example:
One change from id < 100 → id < 20 caused an ~80% drop the system correctly traced it and explained why.
still in early condition though , i was curious though how do u debug issues like this ?
would something like this actually save time or is it just fluff?

I've seen cases where the pipelines were technically "working" but the data itself was slightly off (missing chunks, delayed ingestion, weird values) and no one noticed until dashboards started acting odd.

I am curious about how this will play out in real setups.

Do you take incoming data at face value or have you had instances where something looked ok but was not?

And when that happens… Is it a little thing, or does it really take time to find out?