Alright, let's figure out how to connect Google Analytics (GA4, since Universal Analytics is sunset), Google Search Console (GSC), and other JSON APIs directly to Superset without dumping data into a database. I've tackled similar setups before, so here are the most practical approaches:

Superset relies heavily on SQLAlchemy for data source connections, so building a custom dialect lets you query GA4 using familiar SQL syntax, which gets translated directly to GA4 API calls under the hood.

Here's how to pull this off:

• Set up dependencies: Install the GA4 official SDK and SQLAlchemy:

  pip install google-analytics-data sqlalchemy
  

• Build the custom dialect:
  • Create a Python class that extends sqlalchemy.engine.Dialect
  • Map SQL clauses (like SELECT, WHERE, GROUP BY) to GA4 API parameters (dimensions, metrics, date ranges)
  • For example, translate a query like:

    SELECT sessions, date FROM ga4_property WHERE date BETWEEN '2024-01-01' AND '2024-01-31'
    

    into a GA4 API request with metrics=sessions, dimensions=date, and the specified date range.
• Register the dialect in Superset:
  • Add your dialect to Superset's SQLALCHEMY_DIALECTS config
  • Restart Superset, then add a new data source using your custom dialect
  • Configure the GA4 service account key (store it securely via Superset's secrets manager or environment variables)

If your Superset instance has PySpark support, this is a flexible way to pull data from multiple APIs and expose it to Superset without a database.

Steps:

• Write Spark code to fetch API data:
  • Use the GA4 (google-analytics-data) and GSC (google-searchconsole) SDKs to pull data directly into Spark DataFrames
  • Example snippet for GA4:

    from google.analytics.data_v1beta import BetaAnalyticsDataClient
    from google.analytics.data_v1beta.types import RunReportRequest
    import pyspark.sql.functions as F
    
    client = BetaAnalyticsDataClient()
    request = RunReportRequest(
        property=f"properties/{YOUR_GA4_PROPERTY_ID}",
        date_ranges=[{"start_date": "2024-01-01", "end_date": "2024-01-31"}],
        metrics=[{"name": "sessions"}],
        dimensions=[{"name": "date"}],
    )
    response = client.run_report(request)
    
    # Convert GA4 response to Spark DataFrame
    rows = []
    for row in response.rows:
        rows.append({
            "date": row.dimension_values[0].value,
            "sessions": int(row.metric_values[0].value)
        })
    df = spark.createDataFrame(rows)
    

• Expose the DataFrame to Superset:
  • Register the DataFrame as a temporary view (e.g., df.createOrReplaceTempView("ga4_sessions"))
  • In Superset, add a Spark data source and query the temporary view directly
  • For fresh data every time, set up a scheduled job to refresh the view (or use a Spark streaming job if you need near-real-time data)

If you only need a few specific visualizations and don't want to build a full data source, a custom Superset visualization plugin can fetch data directly from APIs in the frontend.

Notes:

• CORS workaround: Most APIs (including GA4/GSC) don't allow direct frontend calls, so you'll need to set up a backend proxy in Superset to forward API requests
• Build the plugin:
  • Use Superset's visualization template to create a new plugin
  • In the plugin's fetchData method, call the proxy endpoint to get API data
  • Parse the JSON response and render it using a charting library (like ECharts or D3)
• Configure credentials: Store API keys/secrets in Superset's backend and pass them to the proxy endpoint securely

• Authentication:
  • GA4 uses service accounts—make sure to add the service account email as a user in your GA4 property with appropriate permissions
  • GSC requires OAuth2 authentication; set up a token refresh flow in your backend
• Quota Limits: GA4 and GSC have API quotas—enable Superset's caching feature to avoid hitting limits with repeated queries
• Community Plugins: While official GA support is missing, check GitHub for third-party plugins (many were built for Universal Analytics, so you might need to adapt them for GA4)

内容的提问来源于stack exchange，提问作者Antoine Brunel