How to load data from Firebase Realtime Database to BigQuery

Learn how to use Airbyte to synchronize your Firebase Realtime Database data into BigQuery within minutes.

Try Airbyte Cloud Manual method (the hard way)

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Firebase Realtime Database connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Firebase Realtime Database data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Firebase Realtime Database to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync to Manually

Step 1: Export Data from Firebase Realtime Database

Begin by exporting the data from your Firebase Realtime Database. You can do this by using the Firebase Admin SDK in a Node.js environment. Set up a Node.js project, initialize the Firebase Admin SDK with your service account credentials, and use the `firebase-admin` library to retrieve data from your database. Use the `get()` method to read the data and store it in a JSON format.

Step 2: Transform Data into a CSV Format

Once you've retrieved the data in JSON format, transform it into a CSV format which is compatible with BigQuery. You can use JavaScript libraries like `json2csv` to convert JSON to CSV. Ensure that the CSV file includes headers that match the BigQuery schema you plan to use.

Step 3: Set Up Google Cloud SDK

Install and configure the Google Cloud SDK on your local machine. This will allow you to interact with Google Cloud services from the command line. Authenticate your Google Cloud account by running `gcloud auth login` and set the appropriate project using `gcloud config set project [PROJECT_ID]`.

Step 4: Upload CSV to Google Cloud Storage

Create a Google Cloud Storage bucket using the Google Cloud Console or the `gsutil mb` command. Upload your CSV file to this bucket using the `gsutil cp` command. This step is crucial because BigQuery can easily ingest data from Google Cloud Storage.

Step 5: Create a BigQuery Dataset and Table

In the Google Cloud Console, navigate to BigQuery and create a new dataset. Within this dataset, create a table that matches the structure of your CSV data. Define the schema (fields, types, etc.) according to the headers and data types present in your CSV file.

Step 6: Load Data into BigQuery Table

Use the `bq` command-line tool to load your CSV data from Google Cloud Storage into BigQuery. Run a command structured like this: `bq load --source_format=CSV [DATASET].[TABLE] gs://[BUCKET]/[FILE].csv`. Make sure to include options to handle CSV specifics like header rows if needed.

Step 7: Verify Data Integrity in BigQuery

After loading the data, verify its integrity by running a few queries in the BigQuery Console. Check for the correct number of records and spot-check a few entries to ensure that the data appears as expected. Correct any issues by adjusting your CSV file or BigQuery table schema and reloading the data if necessary.
By following these steps, you can successfully transfer data from Firebase Realtime Database to BigQuery without relying on third-party connectors or integrations.