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Google Cloud Firestore to Snowflake

This guide walks you through the process of creating an end-to-end real-time Data Flow from Google Cloud Firestore to Snowflake using Estuary Flow.


You'll need:


In Estuary Flow, you create Data Flows to transfer data from source systems to destination systems in real time. In this use case, your source is an Google Cloud Firestore NoSQL database and your destination is a Snowflake data warehouse.

After following this guide, you'll have a Data Flow that comprises:

  • A capture, which ingests data from Firestore
  • Several collection, cloud-backed copies of Firestore collections in the Flow system
  • A materialization, which pushes the collections to Snowflake

The capture and materialization rely on plug-in components called connectors. We'll walk through how to configure the Firestore and Snowflake connectors to integrate these systems with Flow.

Capture from Firestore

You'll first create a capture to connect to your Firestore database, which will yield one Flow collection for each Firestore collection in your database.

  1. Go to the Flow web application at and sign in using the credentials provided by your Estuary account manager.

  2. Click the Sources tab and choose New Capture.

  3. Find the Google Firestore tile and click Capture.

A form appears with the properties required for a Firestore capture.

  1. Type a name for your capture.

    Your capture name must begin with a prefix to which you have access.

    In the Name field, use the drop-down to select your prefix. Append a unique capture name after the / to create the full name, for example, acmeCo/myFirestoreCapture.

  2. Fill out the required properties for Firestore.

    • Database: Flow can autodetect the database name, but you may optionally specify it here. This is helpful if the service account used has access to multiple Firebase projects. Your database name usually follows the format projects/$PROJECTID/databases/(default).

    • Credentials: The JSON service account key created per the prerequisites.

  3. Click Next.

Flow uses the provided configuration to initiate a connection with Firestore.

It maps each available Firestore collection to a possible Flow collection. It also generates minimal schemas for each collection.

You can use the Source Collections browser to remove or modify collections. You'll have the chance to tighten up each collection's JSON schema later, when you materialize to Snowflake.


If you make any changes to collections, click Next again.

  1. Once you're satisfied with the collections to be captured, click Save and Publish.

You'll see a notification when the capture publishes successfully.

The data currently in your Firestore database has been captured, and future updates to it will be captured continuously.

  1. Click Materialize Collections to continue.

Materialize to Snowflake

Next, you'll add a Snowflake materialization to connect the captured data to its destination: your data warehouse.

  1. Locate the Snowflake tile and click Materialization.

A form appears with the properties required for a Snowflake materialization.

  1. Choose a unique name for your materialization like you did when naming your capture; for example, acmeCo/mySnowflakeMaterialization.

  2. Fill out the required properties for Snowflake (you should have most of these handy from the prerequisites).

    • Host URL
    • Account
    • User
    • Password
    • Database
    • Schema
    • Warehouse: optional
    • Role: optional
  3. Click Next.

Flow uses the provided configuration to initiate a connection to Snowflake.

You'll be notified if there's an error. In that case, fix the configuration form or Snowflake setup as needed and click Next to try again.

Once the connection is successful, the Endpoint Config collapses and the Source Collections browser becomes prominent. It shows the collections you captured previously. Each of them will be mapped to a Snowflake table.

  1. In the Source Collections browser, optionally change the name in the Table field for each collection.

    These will be the names of the output tables in Snowflake.

  2. For each table, choose whether to enable delta updates.

  3. For each collection, apply a stricter schema to be used for the materialization.

Firestore has a flat data structure. To materialize data effectively from Firestore to Snowflake, you should apply a schema can translate to a table structure. Flow's Schema Inference tool can help.

  1. In the Source Collections browser, choose a collection and click its Collection tab.

  2. Click Schema Inference

    The Schema Inference window appears. Flow scans the data in your collection and infers a new schema, called the readSchema, to use for the materialization.

  3. Review the new schema and click Apply Inferred Schema.

  4. Click Next to apply the changes you made to collections.

  5. Click Save and Publish. You'll see a notification when the full Data Flow publishes successfully.

What's next?

Your Data Flow has been deployed, and will run continuously until it's stopped. Updates in your Firestore database will be reflected in your Snowflake table as they occur.

You can advance your Data Flow by adding a derivation. Derivations are real-time data transformations. See the guide to create a derivation.