DataPilot - AI Teammate for Data Teams. The team behind #dbtpoweruser vscode extension

Sunnyvale, California
Joined February 2024
Does your Snowflake cost review start with storage? It misses the pipeline that loads each table. One table cost $0.04 a year to store and $204.93K a year to load.
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So we check reads against writes too. Another table on the same screen had 0 reads against 186 writes in the last month. Check lineage before you drop one, because whole pipelines can come out this way.
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dbt unit tests are the ones teams skip. Altimate Code proposes the test scenarios and the YAML. You pick one. Nothing is added until you approve it. marketplace.visualstudio.com…
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"Show me the 10 most expensive Snowflake queries in the last 30 days, grouped by pattern." Altimate Studio answers in plain English, with the warehouse, the user and a citation link for each query. app.snowflake.com/marketplac…
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One click on a dbt model found 3 issues: 1. A correctness bug in the no-order case 2. A divide by a column that can be zero 3. An aggregation before the join Profile Query in dbt Power User: marketplace.visualstudio.com…
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Your Snowflake compute cost rose 5% overnight. Would anyone notice before the invoice? Write the alert as a sentence, switch it to a 5% rise vs the previous day, and send it to email or Slack. app.snowflake.com/marketplac…
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Dashboard revenue doubled after a dbt change. Which CTE broke it? Profile every CTE and read the row counts. One jumps from 99 to 990 rows. That is your bad join. dbt Power User for VS Code: marketplace.visualstudio.com…
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Which line on your Databricks bill gets the least attention? For one customer it was model serving. 3 endpoints had zero requests for 30 days and still billed $19,800 a month. Altimate flagged them, and the fix saved $240K a year.
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We'd right-size the job first. Auto Tune applies changes between runs and rolls back if runtime passes 1.5x the baseline. When did your team last look at a job's launch template?
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Long dbt models with many ref() calls and macros are hard to follow. The compiled SQL explains the whole model, but you usually need one piece of it.
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Hover over a macro to see what it does and which models use it. Or ask AI to explain one transformation at a time.
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Where do you get lost first in an inherited dbt project: ref() chains or macros?
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Debugging dbt means you untangle Jinja and copy compiled SQL into another tool. The bug often sits in one CTE, and you check them one at a time.
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Profile each CTE to see its row count. A hidden jump in rows points at the bad join, such as a fan-out that duplicates rows.
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How do you find a fan-out join today?
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