- What it is. Root cause analysis for any Snowflake workload. It covers every run of your dbt models, Tableau workloads, notebooks, stored procedures and Streamlit apps. Compare two runs and the AI names the cause: the warehouse, the task count, or the code. dbt and Tableau come in through connections you already made, and Snowflake queries need a tag so their runs group into a workload.
- Who it is for. Analytics engineers, data platform leads and whoever is on call when the nightly job doubles.
- What you get. You find out why the nightly job doubled, without reading three sets of logs.
Root Cause Analysis Starts with Every Snowflake Workload Run
Your nightly job took eighty minutes last night instead of forty. Three tools each hold part of the answer, and none of them holds all of it. So you read the dbt logs, then query history, then the Tableau admin views. By the time you have an answer, the next run has already started.
The Code section reads all of it in one place. You get a run history for each of these:
- dbt models, through the dbt connection you already made.
- Tableau workloads, once the refresh queries carry a tag.
- Notebooks.
- Stored procedures.
- Streamlit apps.
Under any single run you also get the child queries that ran inside it. Cost and run time sit on the same chart. A run that got expensive and a run that got slow are one investigation instead of two.
Here is one workload over 28 days. The blue line is what each run cost you. The purple line is how long each run took. Across 31 runs it averaged 22 minutes 54 seconds and $2.27, and its worst run took 1 hour 6 minutes. That worst run is the one you go and compare.
Each point on this chart is one run. When the run's makeup changes, the chart flags that point as changed. That flag is the task-count cause showing up before you even compare two runs.
Compare Two Runs to Find Out What Changed
The platform's AI compares two runs of the same workload and names the root cause of a spike or a drop. It comes back with one of three things:
| Cause | What it means |
|---|---|
| The warehouse | The warehouse this workload ran on changed. |
| The task count | The workload ran a different number of tasks this time. |
| The code | The code behind the workload changed. |
Three causes will not explain every incident. They do cover the ones your runbook can act on, and the rest usually trace back to one of them. Every workload type goes through the same three checks, whether it is a dbt model or a Streamlit app.
When the warehouse is the cause, you get more than a direction to look in. You get the query's lineage, and a written cause. Here that cause is 202.23 GB spilled to remote storage because the warehouse was sized Small. The recommendation comes with it. Run the query on a larger size, or split it into smaller chunks.
Send the Cause to Jira, Linear, or an Agentic IDE
Once the cause has a name, it stops being an investigation and becomes work. From the opportunity you send it out as a Jira or Linear ticket, or open it in an agentic IDE with the prompt already written.
You choose where the work lands and who owns it. The platform files against Jira or Linear through the Datamate that holds that integration. The ticket reaches the team that already runs on it. Here only Jira is connected.
What matters is what travels with the ticket. It carries the opportunity, the resource and the recommended fix. Whoever picks it up does not have to start by working out what went wrong. If you would rather send it yourself, Studio opens with the prompt already written, and the Datamate already selected.
What You Get Instead of an Hour of Log Reading
- One place for every workload. dbt models, Tableau workloads, notebooks, stored procedures and Streamlit apps. Each keeps its own run history.
- A named cause instead of a theory. The warehouse, the task count, or the code.
- The query that got expensive. Every run lists the child queries that ran inside it, so the finding points at one query and not at the whole job.
- A ticket someone can pick up. Jira, Linear, or an agentic IDE. The opportunity, the resource and the fix are already written into the prompt.
On the workload above, one run took 1 hour 6 minutes against a 22 minute 54 second average. The AI named one cause: 202.23 GB spilled to remote storage on a Small warehouse. You get one query, one reason and one recommendation, rather than a cost line that went up and a shortlist of suspects.
For the wider Snowflake cost picture around this, see Altimate for Snowflake.
