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The Snowflake connector pulls data directly from your warehouse into Novaplan. It is the right choice when your numbers already live in Snowflake (modeled actuals, ARR events, or pipeline) and you want to read them from there rather than from the originating system.

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Set up the sync

1

Connect Snowflake

In Admin -> Integrations, authorize the Snowflake connector with your warehouse credentials.
2

Choose the domain

Set which domain the feed populates (GL actuals, ARR actuals, or pipeline). The domain decides which canonical tables the curated rows land in.
3

Point at your data

Define the query or dataset to read. Each row arrives as a lossless raw payload, exactly as your warehouse returns it.
4

Map fields

Map the warehouse columns onto Novaplan’s canonical fields for that domain. Nova can suggest a mapping from your column names, which you review before saving.
5

Run the first sync

Run the sync, then confirm in Pipelines that rows landed and curated.

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Field mapping

You map warehouse columns to the same canonical fields as any other source for that domain, so the curated result is identical regardless of where it came from.
DomainCanonical fields to map
GL actualsAccount, department, entity / vendor, period, amount, memo
ARR actualsAccount, event month, ARR delta, movement component, customer count delta
PipelineAccount, stage, amount, expected close, owner, segment
Because curation is config-driven and source-agnostic, a Snowflake feed and a CRM feed for the same domain produce the same canonical shape. Switching a domain’s source does not change how the data reads downstream.

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Common questions

Yes, with a separate feed per domain. Each feed maps its own columns to that domain’s canonical fields.
No. The connector reads from your warehouse; it does not modify it.

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Troubleshooting

  • Sync fails to connect: confirm the warehouse is reachable and the credential is valid, then re-run. Network egress restrictions are a common cause for a warehouse that is otherwise healthy.
  • A column will not map: confirm the column is present in the query result, then map it; a column absent from the result cannot be mapped.
  • Dates land in the wrong period: check the date column maps to the canonical period field and normalizes to a month.
  • Rows fail curation: read the per-row reason in Pipelines, fix the mapping or the source value, and re-run.