DATA OPS

On-Demand Drift Investigation and Remediation Plan

Chat-triggered agent that, given a table name, fetches both schemas, explains in plain language exactly how BigQuery and Snowflake diverged, assesses downstream impact.

CategoryData Ops
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerChat: investigate this table
  • ActionRead BigQuery schemaGoogle BigQueryBigQuery
  • ActionRead Snowflake schemaSnowflakeSnowflake
  • LogicReason over diff, impact, and fix orderOpenAI
  • OutputReply with explanation and proposed DDL

What it does

This is an agent you talk to. Ask it about a table and it pulls the live BigQuery and Snowflake schemas, narrates precisely how they differ, reasons about which downstream models or reports the drift would affect, and proposes a concrete reconciliation plan including the DDL it would run. It produces understanding and a plan, not a silent automated change.

When to use it

Use it during an active investigation when a dashboard looks wrong or a dbt run failed and you suspect schema drift. It is the interactive counterpart to the scheduled detectors: ask, get an explanation, and get a vetted plan you can hand off.

How it works

  1. 1A chat message names the table to investigate.
  2. 2The agent reads the BigQuery schema for that table.
  3. 3The agent reads the Snowflake schema for the same table.
  4. 4It reasons over the diff, impact, and a safe remediation order.
  5. 5It replies in chat with a plain-language explanation plus the proposed reconciliation DDL.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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