CHATBOTS

Slack daily schema-change digest for warehouse columns

Each morning the bot diffs the Snowflake catalog against yesterday's snapshot and posts a Slack digest of new, renamed, dropped, and re-typed columns so the team learns about…

CategoryChatbots
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled morning run
  • ActionRead current column inventory from SnowflakeSnowflakeSnowflake
  • ActionLoad prior snapshot and diff from PostgresPostgreSQLPostgres
  • LogicClassify changes; skip if none
  • ActionWrite grouped change digest, breaking changes firstOpenAI
  • OutputPost digest to Slack and save new snapshot to PostgresSlack

What it does

Proactively reports what changed in the warehouse. On a schedule it compares the current Snowflake schema to a stored snapshot and posts a plain-English digest of added, removed, renamed, and type-changed columns to a Slack channel.

When to use it

When silent schema changes break downstream dashboards and nobody finds out until a metric goes blank. Use it to give analysts a daily heads-up and a paper trail of structural changes across key schemas.

How it works

  1. 1A scheduled trigger fires each morning.
  2. 2The bot reads the current column inventory from Snowflake INFORMATION_SCHEMA for the watched schemas.
  3. 3It loads the prior snapshot from Postgres and diffs the two sets to classify adds, drops, renames, and type changes.
  4. 4A logic step skips posting entirely when there are no changes.
  5. 5An OpenAI step writes a concise, grouped digest highlighting breaking changes first.
  6. 6The digest posts to the data Slack channel, and the new snapshot is written back to Postgres for tomorrow's comparison.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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