You can export data from Snowflake Data Cloud to a local file system using the **`PUT`** command. Here are the steps to export data:

1. Connect to your Snowflake account: Log in to your Snowflake account using your credentials.
2. Navigate to the database and table: Navigate to the database and table that contains the data you want to export.
3. Run the **`PUT`** command: Use the **`PUT`** command to export the data to a file in your local file system. For example:

```
PUT file:///path/to/local/file.csv '@my_stage/my_file.csv';

```

In this example, **`file:///path/to/local/file.csv`** is the path to the local file where you want to export the data, and **`@my_stage/my_file.csv`** is the location of the data in your Snowflake internal stage. Note that you can also use an external stage, such as Amazon S3 or Microsoft Azure, as the source for the data.

Once the data is exported to your local file system, you can use it for further processing and analysis using your preferred tools and platforms.

Overall, exporting data from Snowflake Data Cloud to a local file system using the **`PUT`** command is a straightforward process that can be accomplished using familiar SQL commands.

Integrating Google Analytics with Snowflake involves several steps:

1. Set up a Google Analytics account and obtain API access: To access Google Analytics data via API, you will need to create a Google Analytics account and obtain API access credentials. This involves creating a project in the Google Developer Console, enabling the Google Analytics API, and creating a client ID and secret.

2. Set up an integration between Google Analytics and Snowflake: Once you have obtained API access credentials, you can use a third-party integration platform such as Fivetran, Talend, or Stitch to set up an integration between Google Analytics and Snowflake. These platforms provide pre-built connectors that can extract data from Google Analytics, transform it into a format compatible with Snowflake, and load it into a Snowflake table.

3. Query and analyze Google Analytics data in Snowflake: After the data is loaded into Snowflake, you can query and analyze it using SQL. Snowflake supports standard SQL, so you can use familiar SQL commands to join, filter, aggregate, and analyze your Google Analytics data alongside data from other sources.

Here is an example of how to use Fivetran to set up an integration between Google Analytics and Snowflake:

1. Create a Fivetran account: To get started with Fivetran, you will need to create an account and connect it to your Snowflake account.
2. Set up a Google Analytics connector: In the Fivetran dashboard, select Google Analytics as your data source and provide your API access credentials.
3. Configure the Snowflake connector: In the Fivetran dashboard, select Snowflake as your destination and provide your Snowflake account details, including the database and schema where you want to load the data.
4. Customize the data pipeline: Fivetran provides default mappings between Google Analytics data fields and Snowflake table columns, but you can customize the pipeline to include only the data fields you need and transform the data as necessary.
5. Run the data pipeline: Once the data pipeline is set up and configured, you can run it to extract data from Google Analytics, transform it into a format compatible with Snowflake, and load it into a Snowflake table.
6. Query and analyze the data: After the data is loaded into Snowflake, you can query and analyze it using SQL. For example, you can use SQL commands to calculate the number of website visitors, identify the most popular pages, or segment the data by geography or device type.

Overall, integrating Google Analytics with Snowflake using a third-party integration platform such as Fivetran can simplify the data extraction, transformation, and loading process, making it easier to analyze your Google Analytics data alongside data from other sources.