Data Observability cookbook

13 recipes · 90 steps · about 74 minutes of reading

Write your first data SLA
Finance asks if this table's numbers can be trusted, and you realize nobody ever wrote that down.
Beginner to intermediateRead 4 minDoing: an afternoon7 steps
Document what a column means
Three people, three different answers, one column.
Beginner to intermediateRead 5 minDoing: half an hour6 steps
Trace a wrong dashboard number back to its real cause
A stakeholder says the numbers look off, and all you have is a guess about where to start looking.
Beginner to intermediateRead 6 minDoing: an hour or two7 steps
Your loudest data quality alert stopped meaning anything
Half the team has muted the alerts channel.
IntermediateRead 5 minDoing: an afternoon6 steps
Add a range check to a business metric
The pipeline finished green again this morning, exactly like every other morning, and yet the number sitting on the executive dashboard is still wrong.
IntermediateRead 5 minDoing: a couple of hours6 steps
Open source or paid: choose your team's first data observability tool
Open source, or pay for a platform? Leadership wants a real answer, backed by numbers they can act on.
IntermediateRead 5 minDoing: the better part of a week, mostly waiting on a vendor's real quote7 steps
Draft a data contract that survives a schema change
A backend engineer renames one column that looked unused, nobody on the analytics side hears about it in advance, and by morning a revenue report is quietly wrong.
IntermediateRead 5 minDoing: an afternoon7 steps
The same data postmortem keeps repeating, and the fix never sticks
The last incident report just told people to be more careful, and three months later it happened again.
IntermediateRead 5 minDoing: an afternoon7 steps
Flag a field that changed meaning instead of shape
Same field name. Same type. A completely different number underneath.
Intermediate to advancedRead 6 minDoing: a day8 steps
Warn or block: sort your data checks by real risk
Every failing check blocks the deploy, so the team starts merging around the pipeline instead of through it.
Intermediate to advancedRead 6 minDoing: a day6 steps
How many of your tables need paid observability coverage?
A vendor quote is priced for monitoring the entire warehouse. Nobody needs every table in it watched.
AdvancedRead 6 minDoing: the better part of a day, split between a warehouse query, a few quick checks with table owners, and writing up the recommendation7 steps
Trace one field from source to dashboard
Nobody can say for certain what breaks if this one column gets renamed.
AdvancedRead 8 minDoing: an afternoon8 steps
Add a hallucination check to an LLM pipeline step
One stage of your pipeline now runs on a language model that writes plausible-sounding answers, and right now nobody on the team is checking whether those answers are actually true.
AdvancedRead 8 minDoing: a day8 steps

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In these roadmaps

Data Engineerstep 15 of 16

Data Observability cookbook

Write your first data SLA

Document what a column means

Trace a wrong dashboard number back to its real cause

Your loudest data quality alert stopped meaning anything

Add a range check to a business metric

Open source or paid: choose your team's first data observability tool

Draft a data contract that survives a schema change

The same data postmortem keeps repeating, and the fix never sticks

Flag a field that changed meaning instead of shape

Warn or block: sort your data checks by real risk

How many of your tables need paid observability coverage?

Trace one field from source to dashboard

Add a hallucination check to an LLM pipeline step

In these roadmaps

Data Observability cookbook

Write your first data SLA

Document what a column means

Trace a wrong dashboard number back to its real cause

Your loudest data quality alert stopped meaning anything

Add a range check to a business metric

Open source or paid: choose your team's first data observability tool

Draft a data contract that survives a schema change

The same data postmortem keeps repeating, and the fix never sticks

Flag a field that changed meaning instead of shape

Warn or block: sort your data checks by real risk

How many of your tables need paid observability coverage?

Trace one field from source to dashboard

Add a hallucination check to an LLM pipeline step

In these roadmaps