Question 1

Why move Plain data into BigQuery at all?

Accepted Answer

Because the questions worth asking about support usually need data Plain does not hold. Which accounts generate the most threads relative to what they pay, whether response time correlates with churn, how workload splits across tiers: all of that means joining conversations to billing and product tables. Running ETL into BigQuery puts Plain's records in the same place as everything else so analysts can answer those in SQL rather than exporting.

Question 2

How does Plain's nested data map onto BigQuery types?

Accepted Answer

Arrays and objects become BigQuery ARRAY and RECORD columns, which is a close fit for how Plain models conversations. Thread fields arrive as an array of typed objects rather than being expanded into one column per custom field, so you query them with UNNEST. Some relationships are flattened at the source instead, including assignee identifier, name and type and the tier identifier and name, which land as plain scalar columns.

Question 3

Which Plain objects are available?

Accepted Answer

A fixed catalogue: customers, companies, tenants, threads, users, tiers and label types for core records, plus messages, notes, attachments, message components, thread links, timeline events and discussions for conversation detail. Seven prebuilt reports are also exposed, covering new thread daily counts overall and by label, open thread counts by channel and priority, agent daily activity, and SLA compliance percentage by label. Reports are read over bounded time windows rather than cursor paged.

Question 4

Do tenant custom fields come through to BigQuery?

Accepted Answer

Not currently, and this catches people out. Tenant field definitions are specific to your Plain workspace and are discovered automatically, but they exist on the write path: Polytomic can set tenant custom field values in Plain and even provision new ones, while reading from Plain does not fetch those values back. Thread level custom fields behave differently and do arrive, as an array of typed objects on the threads table.

Connect Plain with BigQuery

Connect Plain and BigQuery with Polytomic

Repeated fields, not strings

Support metrics without rebuilding them

Cursor paged, no record cap

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Connect Plain in Polytomic

Connect BigQuery in Polytomic

Create a sync between Plain and BigQuery

Map fields and enable the sync

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