Question 1

How do I load large volumes efficiently?

Accepted Answer

Turn on the Azure Blob Storage mode. Polytomic writes CSV to your container and executes T-SQL BULK INSERT with FORMAT CSV and KEEPNULLS, temporarily creating a database scoped SAS credential and an external data source to reach the file. It is more setup than the default, but it avoids the parameter ceiling that limits batched inserts.

Question 2

What is the default load path?

Accepted Answer

Multi row parameterized INSERT statements into a staging table. Azure SQL caps a statement at two thousand parameters, so batch size is the lesser of a thousand rows or two thousand divided by your column count. On a wide data warehouse table that arithmetic gets uncomfortable quickly, which is exactly when the Blob path earns its extra setup.

Question 3

How does the merge work once data is staged?

Accepted Answer

Not with T-SQL MERGE. Polytomic updates the target from staging by primary key, deletes the matched rows out of staging, then runs INSERT SELECT for whatever remains. Three statements rather than one.

Question 4

Do BigQuery deletes reach Azure SQL?

Accepted Answer

Through full snapshot reconciliation, which marks target rows absent from staging using the deleted at field. With hard delete propagation enabled those marked rows are physically removed instead of flagged.

Question 5

Why push data warehouse results into an operational database?

Accepted Answer

Because the applications and reporting tools that already speak to Azure SQL cannot query BigQuery directly. Landing a modelled result where they connect is usually cheaper and faster than teaching each of them a second data source and a second set of credentials.

Sync BigQuery to Azure SQL

Sync BigQuery to Azure SQL with Polytomic

A real bulk load path

Sidesteps the parameter cap

Or batched inserts

Or sync Azure SQL to BigQuery

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Connect BigQuery in Polytomic

Connect Azure SQL in Polytomic

Create a sync between BigQuery and Azure SQL

Map fields and enable the sync

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