As organizations increasingly rely on data-driven decision-making, the need to migrate data between different cloud data warehouses has become more common. BigQuery and Snowflake are two popular cloud-based data warehousing solutions, each with its own strengths. 

This article explores two methods for migrating data from BigQuery to Snowflake: using Airbyte, an open-source data integration platform, and a manual approach. Whether you're looking to consolidate your data infrastructure or leverage the unique capabilities of Snowflake, this guide will provide you with the knowledge to execute a successful migration.

What is BigQuery?

BigQuery is Google Cloud's fully managed, serverless data warehouse solution. It enables organizations to analyze massive datasets with incredible speed using standard SQL queries. BigQuery's architecture separates computing and storage, allowing for independent scaling and cost optimization. It supports real-time analytics, machine learning integration, and automatic data replication for high availability. BigQuery excels in handling petabyte-scale datasets and offers features like geospatial analysis, a BI engine for faster queries, and seamless integration with other Google Cloud services.

What is Snowflake?

Snowflake is a cloud-native data platform that combines the functionality of a data warehouse, data lake, and data sharing hub. It's built on a unique multi-cluster shared data architecture that separates storage, compute, and services layers for maximum flexibility and performance. Snowflake supports diverse data workloads, including data warehousing, data engineering, data science, and secure data sharing across organizations. Snowflake's platform-agnostic nature allows it to run on multiple cloud providers, offering businesses greater flexibility in their cloud strategy.

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Reasons why a developer might choose to migrate from BigQuery to Snowflake

1. Multi-Cloud Flexibility

Snowflake's platform-agnostic architecture allows deployment across major cloud providers (AWS, Azure, GCP), offering greater flexibility and avoiding vendor lock-in. This multi-cloud strategy enables developers to optimize costs, leverage best-of-breed services, and meet diverse regulatory requirements.

2. Data Sharing

Snowflake's unique data-sharing capabilities allow secure, governed sharing of live data across organizations without data movement or replication. This feature empowers developers to build data ecosystems, monetize data assets, and collaborate seamlessly with partners and customers.

3. Concurrency and Workload Isolation

Snowflake's multi-cluster shared data architecture excels at handling concurrent queries and diverse workloads. By separating compute resources for different tasks (e.g., ETL, analytics, data science), developers can ensure consistent performance without resource contention, a significant advantage over BigQuery's sometimes unpredictable query performance under high concurrency.

4. Access Control

While both platforms offer robust security, Snowflake provides more granular access controls, including column-level security and dynamic data masking. These features give developers greater flexibility in implementing complex data governance policies, especially crucial in highly regulated industries.

Methods to Move Data From BigQuery to Snowflake

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

Method 1: Connecting BigQuery to Snowflake using Airbyte.

Prerequisites

1. A BigQuery account to transfer your customer data automatically from.
2. A Snowflake destination 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 BigQuery and Snowflake destination, for seamless data migration.

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

This allows businesses to leverage their BigQuery data for advanced analytics and insights within Snowflake destination, simplifying the ETL process and saving significant time and resources. Explore our article comparing Snowflake vs. BigQuery to discover how businesses can optimize their data analytics workflow and streamline ETL processes

Step 1: Set up BigQuery as a source connector

1. First, you need to have a Google Cloud Platform account and a project with BigQuery enabled.

2. Go to the Google Cloud Console and create a new service account with the necessary permissions to access your BigQuery data.

3. Download the JSON key file for the service account and keep it safe.

4. Open Airbyte and go to the Sources page.

5. Click on the "Create a new source" button and select "BigQuery" from the list of available sources.

6. Enter a name for your source and click on "Next".

7. In the "Connection Configuration" section, enter the following information:  
- Project ID: the ID of your Google Cloud Platform project  
- JSON Key: copy and paste the contents of the JSON key file you downloaded earlier  
- Dataset: the name of the dataset you want to connect to

8. Click on "Test Connection" to make sure everything is working correctly.

9. If the test is successful, click on "Create Source" to save your configuration.

10. You can now use your BigQuery source connector to extract data from your dataset and load it into Airbyte for further processing.

Step 2: Set up Snowflake destination as a destination connector

1. First, navigate to the Airbyte website and log in to your account.

2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.

3. Scroll down until you find the Snowflake Data Cloud destination connector and click on it.

4. You will be prompted to enter your Snowflake account information, including your account name, username, and password.

5. After entering your account information, click on the "Test" button to ensure that the connection is successful.

6. If the test is successful, click on the "Save" button to save your Snowflake Data Cloud destination connector settings.

7. You can now use the Snowflake Data Cloud destination connector to transfer data from your Airbyte sources to your Snowflake account.

8. To set up a data transfer, navigate to the "Sources" tab on the left-hand side of the screen and select the source you want to transfer data from.

9. Click on the "Create New Connection" button and select the Snowflake Data Cloud destination connector as your destination.

10. Follow the prompts to set up your data transfer, including selecting the tables or data sources you want to transfer and setting up any necessary transformations or mappings.

11. Once you have set up your data transfer, click on the "Run" button to start the transfer process.

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

Once you've successfully connected BigQuery as a data source and Snowflake destination 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 BigQuery from the dropdown list of your configured sources.
3. Select your destination: Choose Snowflake destination 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 BigQuery objects you want to import data from towards Snowflake destination. 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 BigQuery to Snowflake destination according to your settings.

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

Method 2: Connecting BigQuery to Snowflake manually.

Moving data from Google BigQuery to Snowflake without using third-party connectors or integrations involves several steps, including exporting data from BigQuery, transferring the data to a location accessible by Snowflake, and then importing the data into Snowflake. Here's a detailed step-by-step guide:

Step 1: Export Data from BigQuery to Google Cloud Storage

1. Open the BigQuery Console: Navigate to the BigQuery console within your Google Cloud Platform (GCP) account.
2. Select the Dataset and Table: Locate the dataset and table you wish to export.
3. Export Table Data: Use the BigQuery UI or the bq command-line tool to export your table data to Google Cloud Storage (GCS) in a format compatible with Snowflake, such as CSV, JSON, Avro, or Parquet.
   
   For example, using the bq tool:
   bq extract --destination_format CSV 'mydataset.mytable' gs://my-bucket/myfolder/mydata.csv
   
   
4. Replace mydataset.mytable with your dataset and table name, and gs://my-bucket/myfolder/mydata.csv with your GCS bucket and desired file path.

Step 2: Transfer Data to a Location Accessible by Snowflake

1. Choose a Staging Area: Decide on a staging area that Snowflake can access. Snowflake supports data loading from AWS S3, Azure Blob Storage, Google Cloud Storage, and Snowflake’s own staging area.
2. Transfer to Staging Area:some text
   • If you're using GCS as your staging area and your Snowflake account is on GCP, you can use the data directly from GCS.
   • If your Snowflake account is not on GCP, you may need to transfer the data to a supported storage service like AWS S3 or Azure Blob Storage using cloud data transfer services or tools.

Step 3: Create a File Format in Snowflake

1. Login to Snowflake: Access your Snowflake account.
2. Create a File Format: Define a file format that matches the data files you exported from BigQuery.
   For example, for CSV files:
   CREATE FILE FORMAT my_csv_format

TYPE = 'CSV'

FIELD_DELIMITER = ','

SKIP_HEADER = 1

NULL_IF = ('NULL', 'null');

Step 4: Create a Stage in Snowflake

Create a Stage: Set up a stage in Snowflake that points to the location of your data files.
If using GCS:
CREATE STAGE my_gcs_stage

URL = 'gcs://my-bucket/myfolder/'

FILE_FORMAT = my_csv_format

CREDENTIALS = (AWS_KEY_ID = '<your_aws_key_id>' AWS_SECRET_KEY = '<your_aws_secret_key>');

Adjust the URL to point to your GCS bucket and folder, and provide the necessary credentials.

Step 5: Copy Data into Snowflake

1. Create a Target Table: Ensure that you have a target table in Snowflake with a schema that matches the data you're importing.
2. Copy Data: Use the COPY INTO command to load the data from the stage into the target table.
   COPY INTO my_target_table

FROM @my_gcs_stage/mydata.csv

FILE_FORMAT = (FORMAT_NAME = my_csv_format)

ON_ERROR = 'CONTINUE';

3. Replace my_target_table with the name of your target table and adjust the file path if necessary.

Step 6: Verify Data Integrity

1. Check the Loaded Data: After the COPY INTO operation, check the loaded data for any errors or discrepancies.
2. Verify Row Counts: Compare the row counts in Snowflake with the original row counts in BigQuery to ensure completeness.
3. Perform Data Quality Checks: Run queries to validate the data quality, ensuring that the migration process hasn't altered the data.

Step 7: Clean Up

1. Remove Temporary Files: After the data is successfully loaded into Snowflake, remove the temporary files from the staging area to avoid unnecessary storage costs.
2. Delete GCS Data: If you no longer need the exported data in Google Cloud Storage, delete the files to free up space.

Things to note

• Security: Ensure that all data transfers are secure, using encryption in transit and at rest.
• Cost: Be aware of the costs associated with data export, storage, and transfer in both GCP and Snowflake.
• Automation: For recurring data transfers, consider automating the process with scripts or cloud functions.
• Data Types: Make sure that data types are correctly mapped between BigQuery and Snowflake.

By following these steps, you can move data from BigQuery to Snowflake without using third-party connectors or integrations. Always test the process with a subset of data before migrating the entire dataset.

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Use Cases to transfer your BigQuery data to Snowflake destination

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

1. Advanced Analytics: Snowflake destination’s powerful data processing capabilities enable you to perform complex queries and data analysis on your BigQuery data, extracting insights that wouldn't be possible within BigQuery alone.
2. Data Consolidation: If you're using multiple other sources along with BigQuery, syncing to Snowflake destination 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: BigQuery has limits on historical data. Syncing data to Snowflake destination allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Snowflake destination provides robust data security features. Syncing BigQuery data to Snowflake destination ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Snowflake destination can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding BigQuery data.
6. Data Science and Machine Learning: By having BigQuery data in Snowflake destination, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While BigQuery provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Snowflake destination, providing more advanced business intelligence options. If you have a BigQuery table that needs to be converted to a Snowflake destination table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

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