Automatically Export & Sync QuickBooks Online Data to Google BigQuery
Flatly offers several QuickBooks Online data sets that are converted to a raw, non-Report format. These data sets are ideal for data analysis because business intelligence platforms can only ingest data that is structured for machines (flat files), not for humans (accounting reports). Look for the "raw" tag in the data set name. If you prefer a human-formatted QuickBooks Online Report, go with the standard Report data set. Flatly is optimized for data analytics, so expect the non-raw Reports to be quite basic. Note the key difference is that raw Reports adhere to strict conventional column-heading and row-values structure, whereas standard Reports present column headings and values in positions that require a human to infer structure and relations.
"Fantastic Tool"
"If you are looking to extract back-end tables and data to csv, this is the app for you. The interface is easy to use and the tool runs very smoothly."
-- Matt, USA
Flatly's access to Plaid and Plaid's downstream connected financial institutions is read-only access to text. It is limited to account data (words, numbers, dates). It does not include any money-movement, transfer capabilities or account holder profiles/identities.
Plaid alone interfaces directly with financial institutions, caches data from those institutions on Plaid infrastructure, and then makes the appropriate scoped subset of that data available to partners like Flatly using secure connections called client libraries.
Flatly leverages intelligent schema generation that adapts to BigQuery's auto-detection protocols, handling data type inference and column ordering automatically so you never have to manually map fields.
Data hygiene is built directly into the ingestion process. Before a single row is written to BigQuery, Flatly validates your data against a generated reference schema. It proactively detects and strips out "schema violators" —inconsistent data types or malformed records that would typically cause a load job to crash. Flatly logs these exclusions in a detailed metadata file, ensuring your pipeline remains resilient and continuous while giving you full transparency into data quality issues.
Designed for scale and flexibility, the engine processes data in memory-optimized chunks to handle massive datasets efficiently. It offers powerful pre-load controls, allowing you to filter rows based on custom logic (e.g., date ranges, specific values) and allowlist specific keys. This ensures that you only sync high-value data, optimizing your BigQuery storage costs and query performance. Everything is secured via Service Account authentication, providing a stable, production-grade connection for your most critical analytics dashboards.
Connect your data with our turnkey data integration solution so you can focus on running your business.
