> For the complete documentation index, see [llms.txt](https://docs.glassnode.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.glassnode.com/integrations-and-tools/bigquery-data-sharing.md).

# BigQuery Data Sharing

Glassnode offers secure access to historical metrics data through Analytics Hub private listings, allowing you to query our data directly in your own Google Cloud environment.

Data Shares allow you to access the full history range of our metrics without needing to ingest the data through the API, saving you time building a client, managing ETLs, and maintaining data ingestion pipeline infrastructure.

## What are Analytics Hub Listings?

We provide access to our metrics data through Analytics Hub listings. When you subscribe to a listing in [Google Cloud Marketplace](https://console.cloud.google.com/marketplace), BigQuery creates a linked dataset in your Google Cloud project. This linked dataset is read-only and points to the shared Glassnode dataset.

## Getting Started

To access Glassnode's data through Analytics Hub listings, you'll need to provide Glassnode the service account email that should receive access.

### Step 1: Request Access

Share your service account email with your account representative or email us at <sales@glassnode.com>.

The user subscribing to the listing should have the required BigQuery permissions in the destination project, including permission to create the linked dataset.

### Step 2: Access Your Data

Once approved, you'll be able to find the Glassnode datasets in the [Google Cloud Marketplace](https://console.cloud.google.com/marketplace), subscribe to the listing, and create a linked dataset in your BigQuery project.

{% hint style="info" %}
Glassnode BigQuery data shares are currently available only in the `europe-west1` region.
{% endhint %}

For a complete overview of available metrics, see our [Metric Catalog](https://gitlab.com/glassnode/web/docs/-/blob/master/data/metric-catalog/README.md).

{% hint style="warning" %}
**Beta Feature**: During the beta phase, data sharing covers 99% of our metrics. Support for additional metrics will be added as we expand this feature.
{% endhint %}

### How to Access Glassnode Data

Once you've been approved for access, follow these steps:

1. **Find the Glassnode dataset** in the [Google Cloud Marketplace](https://console.cloud.google.com/marketplace) and open the listing. You need to be logged in to the Google Cloud account that has access to the listing.
2. **Click Subscribe** on the listing page.

   <div align="center"><img src="/files/sVlU3uARZ2saq6WH6x9J" alt="Google Marketplace entry" width="60%"></div>
3. **Choose the destination project and linked dataset name**. The linked dataset must be created in the `europe-west1` region. BigQuery will create a read-only linked dataset in your project.
4. **Start querying your data**. Once the linked dataset is created, you can query the shared tables and views from BigQuery like any other dataset.

{% hint style="info" %}
If you cannot see the listing, confirm that you are using the service account shared with Glassnode and that your project has the required BigQuery permissions.
{% endhint %}

### How to Consume Metrics

Once you have access to the Glassnode linked dataset in your BigQuery project, you can explore and query the available metrics:

1. **Browse the linked dataset**: You'll see a set of views in your dataset, with one view per metric and time resolution.
2. **Query a metric**: Use standard SQL and reference the linked dataset in your query:

```
SELECT
  TIMESTAMP,
  A,
  V
FROM `your_project.your_linked_dataset.ADDRESSES__ACTIVE_COUNT_1MONTH`
WHERE A = 'BTC'
ORDER BY TIMESTAMP DESC
LIMIT 100;
```

### Metrics Schema

**Table names** follow the pattern: `{CATEGORY}__{METRIC_NAME}_{RESOLUTION}`

Example: `ADDRESSES__ACTIVE_COUNT_1MONTH`

Each metric table follows a consistent schema structure with three types of fields:

#### Field Types

1. **Timestamp Field**
   * Always named `TIMESTAMP`
   * Represents the time of the data point
   * Type: `TIMESTAMP`
2. **Filter Fields**
   * Used to segment and filter data
   * Follow the same naming convention as query parameters in our API
   * Common filter fields include:
     * `A` - Asset (e.g., BTC, ETH)
     * `NETWORK` - Network
     * Additional filters vary by metric
3. **Value Fields**
   * Contain the actual metric measurements
   * Always prefixed with `V_` for metrics with multiple values
   * Single-value metrics use just `V` (without underscore)
   * Type: Typically `NUMERIC` or `FLOAT64`

#### Example

The metric `/addresses/active_count` with 1-month resolution is stored in table `ADDRESSES__ACTIVE_COUNT_1MONTH`:

| Name      | Type      | Description                              |
| --------- | --------- | ---------------------------------------- |
| TIMESTAMP | TIMESTAMP | Time of the data point                   |
| A         | STRING    | Asset (filter field)                     |
| V         | NUMERIC   | Number of active addresses (value field) |

#### Latency

BigQuery data shares have the same latency as our API.

{% hint style="info" %}
**Ready to get started?** [Contact our team](https://glassnode.com/contact-sales) to learn more about accessing our Analytics Hub listings.
{% endhint %}


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