This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Snowflake as a source connector (using Auth, or usually an API key)
2. set up BigQuery as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Snowflake

Snowflake Data Cloud is a cloud-based data warehousing and analytics platform that allows organizations to store, manage, and analyze large amounts of data in a secure and scalable manner. It provides a single, integrated platform for data storage, processing, and analysis, eliminating the need for multiple tools and systems. Snowflake Data Cloud is built on a unique architecture that separates compute and storage, allowing users to scale up or down as needed without affecting performance. It also offers a range of features such as data sharing, data governance, and machine learning capabilities, making it a comprehensive solution for modern data management and analytics.

What is BigQuery

BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.

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Prerequisites

1. A Snowflake account to transfer your customer data automatically from.
2. A BigQuery account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Snowflake and BigQuery, for seamless data migration.

When using Airbyte to move data from Snowflake to BigQuery, it extracts data from Snowflake using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector.

This allows businesses to leverage their Snowflake data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources. Explore our article on Snowflake vs. BigQuery to discover how seamlessly leveraging your BigQuery data in Snowflake destination streamlines the ETL process, saving valuable time and resources.

Methods to Move Data From Snowflake to bigquery

• Method 1: Connecting Snowflake to bigquery using Airbyte.
• Method 2: Connecting Snowflake to bigquery manually.

Method 1: Connecting Snowflake to bigquery using Airbyte.

Step 1: Set up Snowflake as a source connector

1. First, you need to have a Snowflake Data Cloud account and the necessary credentials to access it.

2. Once you have the credentials, go to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.

3. Click on the "Create a new source" button and select "Snowflake Data Cloud" from the list of available sources.

4. Enter a name for your Snowflake Data Cloud source and click on "Next".

5. In the "Connection" tab, enter the following information:  
- Account name: the name of your Snowflake account  
- Username: your Snowflake username  
- Password: your Snowflake password  
- Warehouse: the name of the warehouse you want to use  
- Database: the name of the database you want to use  
- Schema: the name of the schema you want to use

6. Click on "Test connection" to make sure that the connection is successful.

7. If the connection is successful, click on "Next" to proceed to the "Configuration" tab.

8. In the "Configuration" tab, select the tables or views that you want to replicate and configure any necessary settings.

9. Click on "Create source" to save your Snowflake Data Cloud source and start replicating data.

Step 2: Set up BigQuery as a destination connector

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery destination.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.

Step 3: Set up a connection to sync your Snowflake data to BigQuery

Once you've successfully connected Snowflake as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Snowflake from the dropdown list of your configured sources.
3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Snowflake objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Snowflake to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Snowflake data.

Method 2: Connecting Snowflake to bigquery manually

Moving data from Snowflake to BigQuery without using third-party connectors or integrations involves several steps, including exporting data from Snowflake, storing it in an intermediate location, and then importing it into BigQuery. Below is a step-by-step guide to help you accomplish this:

Step 1: Export Data from Snowflake

1. Log in to Snowflake:

Use the Snowflake web interface or connect through a client tool using your credentials.

2. Select the Data to Export:

Determine which tables or data you want to export from Snowflake.

3. Export the Data to a File:

• Use the `COPY INTO <location>` command to export the data to a file format supported by both Snowflake and BigQuery, such as CSV or Parquet.
• Choose a staging area that Snowflake has access to, such as an Amazon S3 bucket, Azure Blob Storage, or Google Cloud Storage (GCS).

Example command to export data to a CSV file in a GCS bucket:

```sql

COPY INTO 'gcs://<your-bucket-name>/path/to/export/data.csv'

FROM <your_table>

FILE_FORMAT = (TYPE = 'CSV' FIELD_DELIMITER = ',' SKIP_HEADER = 1);

```

4. Verify the Export:

Check that the files have been successfully exported to the specified location.

Step 2: Prepare Google Cloud Storage

1. Set Up Google Cloud Storage:

If you haven't already, create a GCS bucket where you will temporarily store the exported data.

2. Configure Permissions:

Ensure that you have the necessary permissions to read and write to the GCS bucket.

Step 3: Transfer Data to Google Cloud Storage

Transfer Files from Snowflake Staging Area to GCS:

   - If you exported the data directly to GCS, you could skip this step.

   - If the data is in a different location, use `gsutil` or the Google Cloud Console to transfer the files to your GCS bucket.

Example using `gsutil`:

```bash

gsutil cp s3://<your-s3-bucket>/path/to/export/*.csv gs://<your-gcs-bucket>/path/to/import/

```

Step 4: Import Data into BigQuery

1. Access BigQuery:

Open the BigQuery console or use the `bq` command-line tool to interact with BigQuery.

2. Create a Dataset:

If you don't have an existing dataset, create one where you will import the data.

3. Load Data into BigQuery:

Use the BigQuery Data Transfer Service or the `bq load` command to import the data from the GCS bucket into your BigQuery dataset.

Example using the `bq load` command:

```bash

bq load --source_format=CSV <your_dataset>.<your_table> gs://<your-gcs-bucket>/path/to/import/data.csv

```

4. Verify the Import:

Confirm that the data has been imported correctly into BigQuery by querying the tables.

Step 5: Clean Up

1. Remove Temporary Files:

Delete the exported data files from the GCS bucket if they are no longer needed to avoid additional storage costs.

2. Review Security Settings:

Ensure that any temporary access permissions granted for the transfer are revoked.

Use Cases to transfer your Snowflake data to BigQuery

Integrating data from Snowflake to BigQuery provides several benefits. Here are a few use cases:

1. Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Snowflake data, extracting insights that wouldn't be possible within Snowflake alone.
2. Data Consolidation: If you're using multiple other sources along with Snowflake, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Snowflake has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: BigQuery provides robust data security features. Syncing Snowflake data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Snowflake data.
6. Data Science and Machine Learning: By having Snowflake data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Snowflake provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Snowflake table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Snowflake account as an Airbyte data source connector.
2. Configure BigQuery as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Snowflake to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!