Automatically Export & Sync Firestore Data to Google BigQuery
There are no scripts, downloads or extensions required for this integration. Flatly uses Service Account authorization for Firestore. Bring your own Service Account key and then simply enter a path to your Collection.
Flatly syncs Firestore Database data, namely the contents of Documents in specific Collections. It does not sync Project Data like Crashlytics or A/B Testing. Behind the scenes the service first converts Firestore Documents to JSON and then efficiently flattens that JSON into flat files for spreadsheets or storage, or newline JSON for BigQuery.
If you expect your Firestore Collection to contain more than 10 million cells worth of data, do not attempt to sync Firestore to Google Sheets, as Google Sheets is limited to 10 million cells. Instead, use any cloud storage or BigQuery.
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.
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