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Sync Firestore Data to Flat Files & Spreadsheets

Firestore

Automatically sync your Firestore data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.

Historical & Dynamic Data
One-way Sync
For Non-technical Users

About the integration: Cloud Firestore is a flexible, scalable database for mobile, web, and server development from Firebase and Google Cloud. Flatly integrates with Firestore's official APIs.

Connect Firestore Which Firestore API does Flatly use?

Available Datasets

Flatly can sync the following datasets from Firestore.

  • Uniform JSON Objects
  • Varied JSON Objects

Send Firestore data to

Flatly can sync Firestore data to any of these destinations.

Note: Some destinations are aliased, for all intents and purposes they are functionally equivalent.

FAQ: Firestore

Is this project metadata or database data?

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.‍

What if my collection is large?

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.

Querying Firestore - AI or ETL?

Many SaaS applications and databases can be directly queried within AI chat apps using connectors. For ad-hoc, single-user requests, this direct architecture is superior—it offers real-time visibility, high customizability, and bypasses the need for complex data pipelines.

However, ETL (Extract, Transform, Load) can be more favorable when certain requirements exist. You might consider transitioning to an ETL system and a centralized data store when:

  • Collaboration is required: Multiple stakeholders (colleagues, partners) need consistent access to the same shared single source of truth.
  • Downstream audiences: The queried data must be routed into other internal IT systems, published to team dashboards, or preserved in official records rather than remaining isolated in a chat window.
  • Data is complex: The raw data needs heavy cleaning or joining before an AI or non-technical user can accurately understand it.
  • Internal capabilities are limited: Your organization lacks the in-house programming resources or infrastructure necessary to reliably build, deploy, and maintain custom agentic workflows or complex data integrations from scratch.
  • Numerous accounts: When a high volume of identities or accounts within a data source need to be analyzed in bulk, a sophisticated connection manager is needed.
  • Concurrency & infrastructure: If many data sets need to be analyzed in parallel, concurrency can become an issue. The device hosting an AI agent can become saturated without concurrency controls in place, whereas an ETL pipeline is built to handle parallel workloads reliably.