BigQuery Integration

Export Sealmetrics analytics data to Google BigQuery for advanced analysis, custom reporting, and data warehousing.

Overview

The BigQuery integration allows you to:

Automatically sync analytics data to your BigQuery dataset
Run custom SQL queries on your traffic and conversion data
Integrate with BI tools like Looker, Data Studio, or Tableau
Perform advanced analysis not available in the dashboard

Base path: /integrations/bigquery

note

BigQuery integration is available on all plans (Growth, Scale, and Enterprise).

Setup Flow

Create a Google Cloud service account with BigQuery permissions
Configure the integration by uploading the service account JSON file
The dataset and tables are created automatically during setup
Enable automatic sync or trigger manual/backfill syncs

Configure Integration

Create Integration

POST /integrations/bigquery?account_id={account_id}

This endpoint uses query parameters for configuration and a multipart/form-data body to upload the Google Cloud service account JSON file. The dataset and tables are created in BigQuery as part of this call.

Query Parameters:

Parameter	Type	Required	Description
`account_id`	string	Yes	Account ID
`gcp_project_id`	string	Yes	Google Cloud project ID (6-30 chars, lowercase letters, digits and hyphens, starts with a letter)
`dataset_id`	string	No	BigQuery dataset name (alphanumeric and underscores). Default: `sealmetrics`
`location`	enum	No	Dataset location: `EU` (default), `US`, `europe-west1`, `us-central1`, `us-east1`
`sync_frequency`	enum	No	`hourly`, `daily` (default), `manual`
`data_types`	string[]	No	Data types to sync. Default: `traffic`, `conversions`, `microconversions`, `pages`, `landing_pages`. Also valid: `traffic_hourly`, `accounts`
`backfill_days`	integer	No	Days of historical data to sync initially (0-365). Default: `30`
`initial_date_from`	date	No	Custom initial sync start date (overrides `backfill_days`; must be set with `initial_date_to`)
`initial_date_to`	date	No	Custom initial sync end date (must be set with `initial_date_from`)

Form Data (multipart):

Field	Type	Required	Description
`service_account_file`	file	Yes	Google Cloud service account JSON file (must end in `.json` and have `"type": "service_account"`)

note

A custom date range (initial_date_from/initial_date_to) cannot exceed 365 days, and initial_date_to cannot be in the future.

Example request:

curl -X POST "https://api.sealmetrics.com/api/v1/integrations/bigquery?account_id=my-account&gcp_project_id=my-analytics-project&dataset_id=sealmetrics&location=EU&sync_frequency=daily&data_types=traffic&data_types=conversions&backfill_days=30" \
  -H "X-API-Key: sm_your_api_key" \
  -F "service_account_file=@service-account.json"

Response (201 Created):

{
  "integration": {
    "id": 1,
    "account_id": "my-account",
    "gcp_project_id": "my-analytics-project",
    "dataset_id": "sealmetrics",
    "location": "EU",
    "sync_frequency": "daily",
    "data_types": ["traffic", "conversions", "microconversions", "pages", "landing_pages"],
    "backfill_days": 30,
    "initial_date_from": null,
    "initial_date_to": null,
    "is_active": true,
    "setup_completed": true,
    "setup_completed_at": "2025-01-10T14:30:05Z",
    "tables_created": ["fact_traffic_daily", "fact_conversions", "fact_microconversions", "fact_pages", "fact_landing_pages", "sync_metadata", "dim_countries"],
    "last_sync_at": null,
    "last_sync_status": null,
    "last_error": null,
    "created_at": "2025-01-10T14:30:00Z",
    "updated_at": "2025-01-10T14:30:05Z",
    "created_by": 42
  },
  "dataset_ref": "my-analytics-project.sealmetrics",
  "tables_created": ["fact_traffic_daily", "fact_conversions", "fact_microconversions", "fact_pages", "fact_landing_pages", "sync_metadata", "dim_countries"],
  "message": "BigQuery integration configured. Dataset: my-analytics-project.sealmetrics"
}

note

Data is not automatically synced after setup. Use the sync endpoint or wait for a scheduled sync.

Get Integration

GET /integrations/bigquery?account_id={account_id}

Returns null if no integration is configured.

Response:

{
  "id": 1,
  "account_id": "my-account",
  "gcp_project_id": "my-analytics-project",
  "dataset_id": "sealmetrics",
  "location": "EU",
  "sync_frequency": "daily",
  "data_types": ["traffic", "conversions", "microconversions", "pages", "landing_pages"],
  "backfill_days": 30,
  "initial_date_from": null,
  "initial_date_to": null,
  "is_active": true,
  "setup_completed": true,
  "setup_completed_at": "2025-01-01T10:00:05Z",
  "tables_created": ["fact_traffic_daily", "fact_conversions", "fact_microconversions", "fact_pages", "fact_landing_pages", "sync_metadata", "dim_countries"],
  "last_sync_at": "2025-01-10T03:00:00Z",
  "last_sync_status": "success",
  "last_error": null,
  "created_at": "2025-01-01T10:00:00Z",
  "updated_at": "2025-01-10T03:00:00Z",
  "created_by": 42
}

Update Integration

PATCH /integrations/bigquery?account_id={account_id}

Request Body (JSON):

{
  "sync_frequency": "hourly",
  "data_types": ["traffic", "conversions", "microconversions", "pages"],
  "is_active": true,
  "backfill_days": 60
}

Field	Type	Description
`sync_frequency`	enum	`hourly`, `daily`, `manual`
`data_types`	string[]	Which data to sync (at least one)
`is_active`	boolean	Enable/disable sync
`backfill_days`	integer	Historical data range (0-365)

All fields are optional. Only provided fields are updated. If data_types changes, any new tables are created in BigQuery automatically.

Delete Integration

DELETE /integrations/bigquery?account_id={account_id}

Removes the integration configuration. Data already synced to BigQuery is not deleted — you retain full ownership of your BigQuery data.

Response: 204 No Content

Initial Setup

The dataset and tables are created automatically when you create the integration. You can also (re)create the BigQuery dataset and tables explicitly using the stored credentials — useful if the integration was configured but the resources need to be recreated.

POST /integrations/bigquery/setup?account_id={account_id}

This endpoint:

Creates the dataset if it doesn't exist
Creates all tables based on the configured data_types
Always creates the sync_metadata table (for transparency) and the dim_countries lookup table

Response:

{
  "status": "success",
  "dataset_ref": "my-analytics-project.sealmetrics",
  "tables_created": ["fact_traffic_daily", "fact_conversions", "fact_microconversions", "fact_pages", "fact_landing_pages", "sync_metadata", "dim_countries"],
  "location": "EU",
  "message": "BigQuery dataset and tables created successfully"
}

Sync Operations

Trigger Manual Sync

POST /integrations/bigquery/sync?account_id={account_id}

Trigger an immediate incremental sync of recent data. The date range is passed as query parameters (there is no request body).

Query Parameter	Type	Description
`date_from`	date	Start date (default: last sync minus 2 days, or `backfill_days` ago)
`date_to`	date	End date (default: today)

Response:

{
  "status": "success",
  "sync_id": "550e8400-e29b-41d4-a716-446655440000",
  "sync_type": "incremental",
  "date_from": "2025-01-01",
  "date_to": "2025-01-10",
  "tables": {
    "fact_traffic_daily": {"rows": 12000, "bytes": 3200000},
    "fact_conversions": {"rows": 3420, "bytes": 1300000}
  },
  "total_rows": 15420,
  "total_bytes": 4500000,
  "duration_seconds": 45.2,
  "message": "Synced 15,420 rows to BigQuery"
}

Backfill Historical Data

POST /integrations/bigquery/backfill?account_id={account_id}

Sync historical data that may be missing. Backfills are processed in chunks for better progress visibility and resilience; failed chunks are tracked and can be retried.

Request Body (JSON):

{
  "date_from": "2024-01-01",
  "date_to": "2024-12-31",
  "chunk_days": 7
}

Field	Type	Required	Description
`date_from`	date	Yes	Start date for backfill (cannot be in the future)
`date_to`	date	Yes	End date for backfill (cannot be in the future)
`chunk_days`	integer	No	Days per chunk (1-30, default 7). Smaller chunks = more progress updates

caution

A backfill range cannot exceed 365 days. For larger historical imports, run multiple backfills. Backfills consume BigQuery quota and may take several minutes.

Response:

{
  "sync_id": "550e8400-e29b-41d4-a716-446655440001",
  "sync_type": "backfill",
  "date_from": "2024-01-01",
  "date_to": "2024-12-31",
  "status": "success",
  "tables": {
    "fact_traffic_daily": {"rows": 980000, "bytes": 210000000},
    "fact_conversions": {"rows": 270000, "bytes": 90000000}
  },
  "total_rows": 1250000,
  "total_bytes": 300000000,
  "total_chunks": 52,
  "failed_chunks": [],
  "duration_seconds": 540.7,
  "message": "Backfill completed successfully. 1,250,000 rows synced."
}

Retry Failed Sync

POST /integrations/bigquery/retry/{log_id}?account_id={account_id}

Retry a failed sync operation, reusing the date range from the original sync. Only failed or partial syncs can be retried.

Response:

{
  "status": "success",
  "original_log_id": 123,
  "new_sync_id": "550e8400-e29b-41d4-a716-446655440002",
  "sync_type": "incremental",
  "total_rows": 15420,
  "message": "Retry successful. 15,420 rows synced."
}

Sync Logs

List Sync Logs

GET /integrations/bigquery/logs?account_id={account_id}

Query Parameters:

Parameter	Type	Default	Description
`limit`	integer	`20`	Max results (1-100)
`offset`	integer	`0`	Skip N results

Response:

{
  "logs": [
    {
      "id": 123,
      "sync_id": "550e8400-e29b-41d4-a716-446655440000",
      "sync_type": "incremental",
      "sync_started_at": "2025-01-10T03:00:00Z",
      "sync_completed_at": "2025-01-10T03:00:45Z",
      "date_from": "2025-01-09",
      "date_to": "2025-01-09",
      "tables_synced": {
        "fact_traffic_daily": {"rows": 12000, "bytes": 3200000},
        "fact_conversions": {"rows": 3420, "bytes": 1300000}
      },
      "total_rows_synced": 15420,
      "total_bytes_synced": 4500000,
      "status": "success",
      "error_message": null,
      "duration_seconds": 45.2
    }
  ],
  "total": 30
}

Sync Status Values

Status	Description
`running`	Sync in progress
`success`	Sync completed successfully
`partial`	Some tables failed
`failed`	Sync failed completely
`cancelled`	Sync was cancelled

Get Sync Log Detail

GET /integrations/bigquery/logs/{log_id}?account_id={account_id}

Returns detailed information for a single sync operation, including per-table results and any error message.

Schema Information

SealMetrics is a cookieless, aggregated analytics platform. The BigQuery export is a star schema of pre-aggregated daily (and optionally hourly) facts — there is no session-, visitor-, or event-level data. Every fact table is aggregated by date and a set of dimensions (UTM, geo, device, channel).

The tables created depend on the integration's data_types:

Table	`data_type`	Description
`fact_traffic_daily`	`traffic`	Daily traffic with all dimensions (UTM, geo, device, channel)
`fact_traffic_hourly`	`traffic_hourly`	Hourly traffic for intraday analysis (opt-in, 90-day TTL)
`fact_pages`	`pages`	Page performance metrics
`fact_landing_pages`	`landing_pages`	Landing page performance with attribution
`fact_conversions`	`conversions`	Conversion events with full attribution (aggregated by day + dimensions)
`fact_microconversions`	`microconversions`	Microconversion events (form fills, clicks, etc.)
`dim_accounts`	`accounts`	Account metadata for context and JOINs (synced from PostgreSQL)
`dim_countries`	—	ISO 3166-1 country code lookup table (always created, static data)
`sync_metadata`	—	Sync operation metadata for transparency and auditing (always created)

All fact tables are partitioned by date and clustered (primarily by account_id). Every synced row carries sync_id, synced_at, and account_id provenance columns.

Get Table Schema

GET /integrations/bigquery/schema

Returns the schema for all BigQuery tables that SealMetrics creates. This endpoint does not require an account_id — it describes the static star-schema definition.

Response:

{
  "tables": {
    "fact_traffic_daily": {
      "description": "Daily traffic with all dimensions (UTM, geo, device, channel)",
      "data_type": "traffic",
      "schema": [
        {"name": "sync_id", "type": "STRING", "mode": "REQUIRED"},
        {"name": "synced_at", "type": "TIMESTAMP", "mode": "REQUIRED"},
        {"name": "account_id", "type": "STRING", "mode": "REQUIRED"},
        {"name": "date", "type": "DATE", "mode": "REQUIRED"},
        {"name": "utm_source", "type": "STRING", "mode": "NULLABLE"},
        {"name": "utm_medium", "type": "STRING", "mode": "NULLABLE"},
        {"name": "utm_campaign", "type": "STRING", "mode": "NULLABLE"},
        {"name": "utm_term", "type": "STRING", "mode": "NULLABLE"},
        {"name": "utm_content", "type": "STRING", "mode": "NULLABLE"},
        {"name": "channel_group", "type": "STRING", "mode": "NULLABLE"},
        {"name": "country", "type": "STRING", "mode": "NULLABLE"},
        {"name": "device_type", "type": "STRING", "mode": "NULLABLE"},
        {"name": "browser", "type": "STRING", "mode": "NULLABLE"},
        {"name": "os", "type": "STRING", "mode": "NULLABLE"},
        {"name": "day_of_week", "type": "INT64", "mode": "NULLABLE"},
        {"name": "entrances", "type": "INT64", "mode": "NULLABLE"},
        {"name": "engaged_entrances", "type": "INT64", "mode": "NULLABLE"},
        {"name": "page_views", "type": "INT64", "mode": "NULLABLE"},
        {"name": "microconversions", "type": "INT64", "mode": "NULLABLE"},
        {"name": "conversions", "type": "INT64", "mode": "NULLABLE"},
        {"name": "revenue", "type": "NUMERIC", "mode": "NULLABLE"}
      ],
      "partition_field": "date",
      "clustering_fields": ["account_id", "utm_source", "country"],
      "partition_expiration_days": null
    }
  }
}

Each table entry includes description, data_type, the full schema (field name, type, mode), partition_field, clustering_fields, and partition_expiration_days (e.g. 90 for fact_traffic_hourly).

Table Fields

`fact_traffic_daily` / `fact_traffic_hourly`

Aggregated traffic. fact_traffic_hourly adds an hour (0-23) column and omits utm_campaign/utm_term/utm_content; it has a 90-day partition expiration.

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the row was synced
`account_id`	STRING	Account identifier
`date`	DATE	Aggregation day (partition field)
`hour`	INT64	Hour 0-23 (`fact_traffic_hourly` only)
`utm_source` / `utm_medium` / `utm_campaign` / `utm_term` / `utm_content`	STRING	UTM dimensions
`channel_group`	STRING	Channel grouping
`country`	STRING	Country code (ISO)
`device_type` / `browser` / `os`	STRING	Device/tech dimensions
`day_of_week`	INT64	ISO day of week (1=Mon, 7=Sun)
`entrances`	INT64	Entrances
`engaged_entrances`	INT64	Engaged entrances
`page_views`	INT64	Page views
`microconversions`	INT64	Microconversions
`conversions`	INT64	Conversions
`revenue`	NUMERIC	Revenue

`fact_pages`

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the row was synced
`account_id`	STRING	Account identifier
`date`	DATE	Aggregation day (partition field)
`page_path`	STRING	Page path
`content_grouping`	STRING	Content grouping
`country`	STRING	Country code (ISO)
`channel_group`	STRING	Channel grouping
`entrances`	INT64	Entrances
`engaged_entrances`	INT64	Engaged entrances
`page_views`	INT64	Page views

`fact_landing_pages`

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the row was synced
`account_id`	STRING	Account identifier
`date`	DATE	Aggregation day (partition field)
`landing_page`	STRING	Landing page path
`content_grouping`	STRING	Content grouping
`utm_source` / `utm_medium`	STRING	UTM dimensions
`channel_group`	STRING	Channel grouping
`country`	STRING	Country code (ISO)
`entrances`	INT64	Entrances
`engaged_entrances`	INT64	Engaged entrances
`microconversions`	INT64	Microconversions
`conversions`	INT64	Conversions
`revenue`	NUMERIC	Revenue

`fact_conversions`

Conversion events aggregated by day and attribution dimensions.

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the row was synced
`account_id`	STRING	Account identifier
`date`	DATE	Aggregation day (partition field)
`conversion_type`	STRING	Conversion type
`utm_source` / `utm_medium` / `utm_campaign` / `utm_term` / `utm_content`	STRING	UTM dimensions
`channel_group`	STRING	Channel grouping
`country`	STRING	Country code (ISO)
`device_type` / `browser` / `os`	STRING	Device/tech dimensions
`landing_page`	STRING	Landing page path
`click_id`	STRING	Click identifier (clid)
`count`	INT64	Number of conversions
`amount`	NUMERIC	Per-conversion value
`revenue`	NUMERIC	Total revenue
`properties`	JSON	Conversion properties

`fact_microconversions`

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the row was synced
`account_id`	STRING	Account identifier
`date`	DATE	Aggregation day (partition field)
`conversion_type`	STRING	Microconversion type
`utm_source` / `utm_medium` / `utm_campaign`	STRING	UTM dimensions
`channel_group`	STRING	Channel grouping
`country`	STRING	Country code (ISO)
`device_type`	STRING	Device type
`count`	INT64	Number of microconversions
`properties`	JSON	Event properties

`dim_accounts`

Account metadata (synced from PostgreSQL when the accounts data type is enabled).

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the row was synced
`account_id`	STRING	Account identifier
`account_name`	STRING	Account name
`timezone`	STRING	Account timezone
`currency`	STRING	Account currency
`plan_tier`	STRING	Plan tier
`organization_name`	STRING	Organization name
`created_at`	TIMESTAMP	Account creation time
`is_active`	BOOL	Whether the account is active

`dim_countries`

Static ISO 3166-1 lookup, always created.

Field	Type	Description
`country_code`	STRING	ISO 3166-1 country code
`country_name`	STRING	Country name
`continent`	STRING	Continent
`region`	STRING	Region

`sync_metadata`

Per-sync audit record, always created.

Field	Type	Description
`sync_id`	STRING	Sync operation identifier
`synced_at`	TIMESTAMP	When the sync ran
`sync_type`	STRING	`incremental` or `backfill`
`date_from`	DATE	Start of synced range
`date_to`	DATE	End of synced range
`tables_synced`	STRING (REPEATED)	Tables included in the sync
`total_rows`	INT64	Total rows synced
`duration_seconds`	FLOAT64	Sync duration
`sealmetrics_version`	STRING	SealMetrics export version

Required Permissions

The service account needs these BigQuery permissions:

Permission	Purpose
`bigquery.datasets.create`	Create dataset (if needed)
`bigquery.datasets.get`	Read dataset metadata
`bigquery.tables.create`	Create tables
`bigquery.tables.get`	Read table metadata
`bigquery.tables.updateData`	Insert data
`bigquery.jobs.create`	Run load jobs

Recommended Role: roles/bigquery.dataEditor on the dataset.

Code Examples

Python - Setup and Sync

import requests

API_KEY = "sm_your_api_key"
BASE_URL = "https://api.sealmetrics.com/api/v1"
ACCOUNT_ID = "my-account"

def setup_bigquery(gcp_project_id, dataset_id, credentials_path):
    """Configure BigQuery integration (multipart file upload)."""

    # Configuration goes in query params; the service account JSON is uploaded
    # as a multipart file. The dataset and tables are created automatically.
    with open(credentials_path, "rb") as f:
        response = requests.post(
            f"{BASE_URL}/integrations/bigquery",
            headers={"X-API-Key": API_KEY},
            params={
                "account_id": ACCOUNT_ID,
                "gcp_project_id": gcp_project_id,
                "dataset_id": dataset_id,
                "location": "EU",
                "sync_frequency": "daily",
                "data_types": ["traffic", "conversions", "microconversions",
                               "pages", "landing_pages"],
                "backfill_days": 30,
            },
            files={"service_account_file": ("service-account.json", f, "application/json")},
        )
    response.raise_for_status()
    return response.json()


def trigger_backfill(date_from, date_to):
    """Backfill historical data (max 365 days)."""
    response = requests.post(
        f"{BASE_URL}/integrations/bigquery/backfill",
        headers={"X-API-Key": API_KEY},
        params={"account_id": ACCOUNT_ID},
        json={
            "date_from": date_from,
            "date_to": date_to,
            "chunk_days": 7,
        }
    )
    response.raise_for_status()
    return response.json()


def check_sync_status(sync_id):
    """Check status of a sync operation."""
    response = requests.get(
        f"{BASE_URL}/integrations/bigquery/logs",
        headers={"X-API-Key": API_KEY},
        params={"account_id": ACCOUNT_ID}
    )

    for log in response.json()["logs"]:
        if str(log["sync_id"]) == str(sync_id):
            return log["status"]

    return "not_found"

JavaScript - Monitor Sync

async function monitorSync(syncId) {
  const checkStatus = async () => {
    const response = await fetch(
      `${BASE_URL}/integrations/bigquery/logs?account_id=${ACCOUNT_ID}`,
      {
        headers: { 'X-API-Key': API_KEY }
      }
    );

    const { logs } = await response.json();
    const log = logs.find(l => l.sync_id === syncId);

    return log?.status || 'not_found';
  };

  let status = await checkStatus();

  while (status === 'running') {
    await new Promise(r => setTimeout(r, 5000)); // Wait 5s
    status = await checkStatus();
    console.log(`Sync status: ${status}`);
  }

  return status;
}

Error Codes

HTTP Code	Error	Description
400	`invalid_credentials`	Service account JSON is malformed
401	`authentication_failed`	Credentials rejected by Google
403	`insufficient_permissions`	Missing required BigQuery permissions
404	`integration_not_found`	No BigQuery integration configured
409	`sync_in_progress`	Another sync is already running
429	`quota_exceeded`	BigQuery API quota exceeded

Best Practices

1. Use Dedicated Service Account

Create a service account specifically for Sealmetrics:

gcloud iam service-accounts create sealmetrics-sync \
  --display-name="Sealmetrics BigQuery Sync"

2. Limit Dataset Permissions

Grant permissions only on the specific dataset:

bq add-iam-policy-binding \
  --member="serviceAccount:sealmetrics-sync@project.iam.gserviceaccount.com" \
  --role="roles/bigquery.dataEditor" \
  project:sealmetrics

3. Choose the Right Sync Frequency

Set sync_frequency to daily (default) for most use cases, hourly for near-real-time intraday analysis, or manual to trigger syncs only on demand via the sync endpoint.

Use the default location of EU (or europe-west1) to keep your aggregated analytics data within the EU.

5. Monitor Sync Logs

Regularly check sync logs for failures and watch for the failed or partial status.

Overview​

Setup Flow​

Configure Integration​

Create Integration​

Get Integration​

Update Integration​

Delete Integration​

Initial Setup​

Sync Operations​

Trigger Manual Sync​

Backfill Historical Data​

Retry Failed Sync​

Sync Logs​

List Sync Logs​

Sync Status Values​

Get Sync Log Detail​

Schema Information​

Get Table Schema​

Table Fields​

fact_traffic_daily / fact_traffic_hourly​

fact_pages​

fact_landing_pages​

fact_conversions​

fact_microconversions​

dim_accounts​

dim_countries​

sync_metadata​

Required Permissions​

Code Examples​

Python - Setup and Sync​

JavaScript - Monitor Sync​

Error Codes​

Best Practices​

1. Use Dedicated Service Account​

2. Limit Dataset Permissions​

3. Choose the Right Sync Frequency​

4. Keep Data in the EU for GDPR​

5. Monitor Sync Logs​

Overview

Setup Flow

Configure Integration

Create Integration

Get Integration

Update Integration

Delete Integration

Initial Setup

Sync Operations

Trigger Manual Sync

Backfill Historical Data

Retry Failed Sync

Sync Logs

List Sync Logs

Sync Status Values

Get Sync Log Detail

Schema Information

Get Table Schema

Table Fields

`fact_traffic_daily` / `fact_traffic_hourly`

`fact_pages`

`fact_landing_pages`

`fact_conversions`

`fact_microconversions`

`dim_accounts`

`dim_countries`

`sync_metadata`

Required Permissions

Code Examples

Python - Setup and Sync

JavaScript - Monitor Sync

Error Codes

Best Practices

1. Use Dedicated Service Account

2. Limit Dataset Permissions

3. Choose the Right Sync Frequency

4. Keep Data in the EU for GDPR

5. Monitor Sync Logs