How to Migrate from BigQuery to Snowflake

Are you considering a move from Google BigQuery to Snowflake? You’re not alone.

As businesses evolve, so do their data needs. This guide will walk you through the process of migrating from BigQuery to Snowflake, covering everything from the basics to advanced techniques.

We’ll cover:

• Understanding BigQuery and Snowflake
• Reasons for migrating
• Common challenges and how to overcome them
• Benefits of migrating to Snowflake
• Top migration methods
• A step-by-step walkthrough using Coefficient
• Best practices for a successful migration

Let’s dive in and explore how to migrate from BigQuery to Snowflake effectively.

Why Migrate from BigQuery to Snowflake?

Before we get into the nitty-gritty of migration, let’s take a moment to understand these two powerhouse data warehouses.

What is Google BigQuery?

BigQuery is Google Cloud’s fully managed, serverless data warehouse. It’s known for its ability to analyze massive datasets quickly using SQL queries. Some key features include:

• Automatic scaling and resource management
• Real-time analytics
• Machine learning capabilities
• Integration with Google Cloud services

What is Snowflake?

Snowflake is a cloud-native data warehouse that offers a unique architecture separating storage and compute. Key features include:

• Multi-cloud support (AWS, Azure, Google Cloud)
• Near-infinite scalability
• Data sharing capabilities
• Support for semi-structured data

Key Differences

1. Architecture: BigQuery uses a shared-nothing architecture, while Snowflake separates storage and compute.
2. Pricing: BigQuery charges based on data scanned, while Snowflake uses a credit-based system.
3. Data sharing: Snowflake offers more advanced data sharing capabilities.
4. Multi-cloud support: Snowflake can run on multiple cloud platforms, while BigQuery is limited to Google Cloud.

Organizations choose to migrate from BigQuery to Snowflake for various reasons:

Benefits of Migrating from BigQuery to Snowflake

While BigQuery is a powerful tool, Snowflake offers several unique advantages:

Cost Efficiency

Snowflake’s pricing model can be more cost-effective for certain workloads:

• Pay only for the compute resources you use
• Ability to scale up or down instantly
• Separate storage and compute costs for better optimization

Performance Improvements

Snowflake’s architecture can offer performance benefits:

• Automatic query optimization
• Instant scaling of compute resources
• Caching of query results for improved performance

Multi-Cloud Flexibility

Snowflake’s multi-cloud support provides:

• Freedom to choose or switch cloud providers
• Ability to meet data residency requirements
• Easier disaster recovery and business continuity planning

Advanced Data Sharing

Snowflake’s data sharing capabilities allow:

• Secure sharing of live data without copying or moving it
• Creation of data marketplaces
• Simplified collaboration with external partners

Common Challenges in BigQuery to Snowflake Migration

Migrating between data warehouses isn’t without its hurdles. Here are some common challenges you might face:

Schema Differences

BigQuery and Snowflake handle schemas differently:

• BigQuery uses a flat schema structure, while Snowflake uses a more traditional hierarchical structure.
• Data types may not map directly between the two platforms.

To address this:

1. Carefully review and map data types between BigQuery and Snowflake.
2. Use Snowflake’s VARIANT data type for complex nested structures from BigQuery.
3. Consider using a tool like dbt for schema management during migration.

Performance Optimization

Query performance may differ between BigQuery and Snowflake due to their architectural differences.

To optimize performance:

1. Review and adjust query patterns for Snowflake’s architecture.
2. Utilize Snowflake’s clustering keys for frequently filtered columns.
3. Take advantage of Snowflake’s materialized views for common query patterns.

Data Transfer and Downtime

Minimizing downtime and ensuring data integrity during migration is crucial.

To manage this:

1. Plan for incremental data transfers to reduce downtime.
2. Use Snowflake’s data loading features like Snowpipe for continuous data ingestion.
3. Implement a robust testing strategy to verify data accuracy post-migration.

Cost Management

Understanding the cost implications of migration is essential for budget planning.

To manage costs effectively:

1. Use Snowflake’s resource monitors to set spending limits.
2. Implement auto-suspend and auto-resume features for compute resources.
3. Regularly review and optimize warehouse sizes and usage patterns.

Top Methods for Migrating BigQuery to Snowflake

Let’s explore the main approaches to migrating your data:

Snowflake’s BigQuery Connector

Snowflake provides a native BigQuery connector that simplifies the migration process.

How to use:

Set up the BigQuery connector in Snowflake:

CREATE OR REPLACE EXTERNAL FUNCTION bigquery_query(query STRING)

RETURNS VARIANT

API_INTEGRATION = bigquery_api_integration

1. AS ‘https://us-central1-bigquery-connector.cloudfunctions.net/bigquery_query’;
   
2. Query BigQuery data directly from Snowflake:
   
   SELECT * FROM TABLE(bigquery_query(‘SELECT * FROM `project.dataset.table`’));
   

Use Snowflake’s COPY INTO command to load data:

COPY INTO snowflake_table

FROM (SELECT * FROM TABLE(bigquery_query(‘SELECT * FROM `project.dataset.table`’)))

1. FILE_FORMAT = (TYPE = JSON);
   

Pros:

• Native integration
• Simplifies data transfer
• Supports incremental loading

Cons:

• May require additional setup
• Limited to querying and copying data

Custom ETL Scripts

Writing custom scripts gives you full control over the migration process.

Example Python script using Google Cloud and Snowflake libraries:

from google.cloud import bigquery

from snowflake.connector import connect

# Connect to BigQuery

bq_client = bigquery.Client()

# Connect to Snowflake

sf_conn = connect(

    account=’your_account’,

    user=’your_user’,

    password=’your_password’,

    warehouse=’your_warehouse’,

    database=’your_database’,

    schema=’your_schema’

)

# Query BigQuery

query = “SELECT * FROM `project.dataset.table`”

results = bq_client.query(query)

# Insert into Snowflake

with sf_conn.cursor() as cur:

    for row in results:

        cur.execute(

            “INSERT INTO your_table (col1, col2) VALUES (%s, %s)”,

            (row[‘col1’], row[‘col2’])

        )

sf_conn.commit()

sf_conn.close()

Pros:

• Full control over the migration process
• Can handle complex transformations
• Customizable error handling and logging

Cons:

• Requires programming skills
• May be time-consuming to develop and maintain

Third-Party Migration Tools

Several third-party tools can facilitate BigQuery to Snowflake migration:

1. Fivetran
   • Offers pre-built connectors for both BigQuery and Snowflake
   • Supports incremental updates and schema changes
   • Provides monitoring and error handling

1. Matillion
   • Visual ETL tool with components for both BigQuery and Snowflake
   • Supports complex transformations
   • Offers cloud-native deployment

1. Talend
   • Comprehensive data integration platform
   • Provides visual mapping and transformation tools
   • Supports real-time and batch processing

Coefficient’s Data Migration Solution

Coefficient offers a powerful solution for migrating data from BigQuery to Snowflake, with additional features for data analysis and reporting.

Key features:

• User-friendly interface for mapping and transforming data
• Support for incremental updates
• Built-in data quality checks and error handling
• Integration with spreadsheet tools for easy data manipulation

While Coefficient doesn’t currently offer a direct BigQuery to Snowflake migration tool, it provides integrations that can be useful in the migration process:

• BigQuery Integration for Google Sheets
• BigQuery Integration for Excel
• Snowflake Integration for Google Sheets
• Snowflake Integration for Excel

These integrations can be used to extract data from BigQuery, perform any necessary transformations in a spreadsheet, and then load the data into Snowflake.

Supporting Your BigQuery to Snowflake Migration with Coefficient

While not a direct migration tool, Coefficient can significantly support your BigQuery to Snowflake migration process. It’s a powerful data sync tool that pulls live data from various sources, including BigQuery and Snowflake, into spreadsheets like Google Sheets or Excel.

This capability makes it easier to examine your data, create reports, and validate your migration without interfering with your live databases.

Benefits of Using Coefficient for SQL Server to Snowflake Migration

Pre-Migration Data Analysis:

• Connect Coefficient to your BigQuery project and analyze data structures.
• Create detailed reports on data volume, types, and usage patterns to inform your migration strategy.
• Identify potential issues or complexities before beginning the migration process.

Transitional Reporting:

• During the migration, use Coefficient to create blended reports pulling data from both BigQuery and Snowflake.
• Ensure business continuity by providing stakeholders with up-to-date information from both systems.
• Perform real-time comparisons of data between the old and new databases to catch discrepancies early.

Post-Migration Validation:

• After migration, use Coefficient’s data sync features to compare data between BigQuery and Snowflake.
• Set up automated checks to ensure data integrity and identify any inconsistencies.
• Create dashboards to visualize the success of your migration and highlight any areas needing attention.

Data Cleanup and Enrichment:

• Use Coefficient to identify and clean up data inconsistencies in the new Snowflake database.
• Leverage Coefficient’s “Export to Snowflake” feature to send corrected or enriched data back to Snowflake.
• Streamline post-migration data quality processes with automated workflows.

How to Migrate Data from BigQuery to Snowflake with Coefficient

Step 1. Install and Setup Coefficient:

• Install the Coefficient add-on for Google Sheets or add-in for Excel
• Launch Coefficient from the Extensions menu and authorize access to BigQuery.

Step 2. Import Data from BigQuery:

• In Coefficient, select “Import from…” > “BigQuery” > “From Tables & Columns”

• Choose the BigQuery table and customize your import (columns, filters, etc.).

• Click “Import” to load the data into your spreadsheet

Step 3. Prepare Data in Google Sheets/Excel

Once the data is imported, it will appear in your spreadsheet. Review and organize the data to ensure:

• Proper headers are set.
• Data accuracy is verified.
• Necessary transformations are made to fit the right schema.

Step 4. Export Data to Snowflake

Finally, push the data to Snowflake:

• In the Coefficient sidebar, click “Export to…” and then “Snowflake”.

• Select the action (Insert, Update, Delete) and confirm your settings.

• Designate the appropriate Snowflake table and complete field mappings.

• Confirm your settings and then click “Export”.

When the Export to Snowflake is complete (and successful), you can see the number of rows exported/skipped.

BigQuery to Snowflake: Tips and Best Practices

Thorough Planning and Testing

• Develop a detailed migration plan, including timelines and resource allocation
• Create a test environment to validate migration processes before going live
• Perform multiple dry runs to identify and resolve issues

Data Validation and Reconciliation

• Implement checksums to verify data integrity during transfer
• Use Snowflake’s data validation features to ensure accuracy
• Perform row counts and data sampling to catch any discrepancies

Performance Monitoring and Optimization

• Utilize Snowflake’s query profiling tools to identify performance bottlenecks
• Regularly review and optimize warehouse sizes and usage patterns
• Implement proper clustering keys and materialized views for frequently accessed data

Training and Documentation

• Provide comprehensive training for team members on Snowflake’s features and best practices
• Create detailed documentation of the migration process and new data architecture
• Establish guidelines for ongoing data management in Snowflake

BigQuery to Snowflake: Powering Up Your Data Warehouse

Migrating from BigQuery to Snowflake opens doors to enhanced scalability and advanced analytics capabilities. Success hinges on meticulous planning and execution, focusing on data integrity and minimal operational disruption.

Post-migration, prioritize optimizing your Snowflake implementation. Leverage its unique features like dynamic data masking and secure data sharing to enhance your data operations. Ensure your team is equipped to fully utilize Snowflake’s robust ecosystem.

Ready to amplify your Snowflake’s potential? Learn how to connect your Snowflake data directly to Excel and Google Sheets for seamless analysis and reporting.