DATA OPS

Operational Postgres Source Drift Watcher

Monitors application Postgres tables that feed the warehouse, detects when a source schema changes ahead of its Snowflake ingest contract.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled source check
  • ActionRead table definitions from operational PostgresPostgreSQLPostgres
  • ActionRead agreed landing contract from SnowflakeSnowflakeSnowflake
  • LogicDiff source vs contract; flag unprepared columns
  • OutputOpen Linear ticket and post Slack heads-upLinearLinear

What it does

Schema drift usually starts upstream, in the operational database, before it ever reaches the warehouse. This workflow watches the application Postgres tables that feed ingestion, compares their current shape to the agreed Snowflake landing contract, and raises an alert the instant the source moves ahead of the contract — giving the warehouse team lead time before ingestion breaks.

When to use it

Use it when product engineers ship migrations that silently change the tables your ETL reads. Catching the change at the source, not after a failed load, prevents broken pipelines and dropped rows.

How it works

  1. 1A schedule starts the source check.
  2. 2Read current table definitions from the operational Postgres database.
  3. 3Read the agreed landing contract registered in Snowflake.
  4. 4Diff source against contract and flag any column the warehouse is not prepared to receive.
  5. 5If drift is found, open a Linear ticket for the data team and post a Slack heads-up to the owning service team.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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