Question 1

What QuickBooks data can I load into Databricks?

Accepted Answer

Most of the accounting model: accounts, bills and bill payments, budgets, classes, credit memos, customers and customer types, departments, deposits, employees, estimates, invoices, items, journal entries, payments and payment methods, purchases and purchase orders, refund and sales receipts, tax agencies, codes and rates, terms, time activities, transfers, vendors and vendor credits. Reports come too, including general ledger, balance sheet, vendor expenses, customer balance and cash flow, in cash or accrual.

Question 2

Will ledger amounts keep their precision?

Accepted Answer

Yes. Decimal values map to DECIMAL with their precision and scale carried across, and values without a declared precision fall back to a wide DECIMAL rather than a floating point type. That matters more for a ledger than for most sources, since a rounding difference in a general ledger table tends to surface during a reconciliation rather than during testing. Dates and timestamps map to their native types, so period filtering behaves normally.

Question 3

Are voided QuickBooks transactions removed?

Accepted Answer

Yes, for the objects QuickBooks includes in its change tracking feed. The connector reads that feed, treats a deleted status as a real delete, and applies it with a one day overlap and up to a ten day lookback. The row is marked with a deleted at timestamp by default, or removed outright with hard deletes enabled, which requires the Polytomic timestamp columns to stay in place. Plain last updated reads do not surface deletions at all.

Question 4

Which QuickBooks products are supported?

Accepted Answer

QuickBooks Online editions, Advanced included, because the connector runs on the QuickBooks Online Accounting API. Desktop and Self-Employed are not supported, since a Desktop company file is a local artefact rather than something reachable over that API. Confirm which product your finance team actually uses before scoping, as this is the difference between a straightforward setup and a migration.

Question 5

Why put accounting data in a lakehouse?

Accepted Answer

Because finance data is the piece that sits on its own, and the questions people ask cut across it. Which customers are genuinely profitable, how revenue tracks against pipeline, what a cohort is worth after costs, none of which the accounting tool can answer. ETL into a data warehouse makes those joins possible and puts the figures within reach of forecasting work rather than in a separate export.

Sync Quickbooks to Databricks

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Decimal precision kept

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