Case Study: FantasySports Migrating Databases from Snowflake to BigQuery for Enhanced Performance and Cost Efficiency
Client Overview: Our client, a prominent organization with a data-driven business model, managed over 5TB of data across 20+ datasets in two distinct projects on Snowflake. Despite Snowflake’s robust capabilities, rising costs and a need for improved integration with their broader data ecosystem prompted the client to explore alternatives. Google BigQuery (BQ) emerged as the ideal solution, promising scalability, cost efficiency, and seamless integration with other Google Cloud Platform (GCP) services.
Challenges: The migration of databases from Snowflake to BigQuery posed several challenges:
Large Data Volume: With over 5TB of data to migrate, ensuring minimal disruption to operations was critical.
Complex Dataset Structure: The client’s data spanned 20+ datasets across two projects, requiring meticulous planning for efficient data organization and accessibility.
ETL Transition: The client used Hevo, an ETL tool, to stream data from MongoDB to Snowflake. Post-migration, this setup needed reconfiguration to redirect streams to BigQuery.
Dashboard Migration: The client relied on Power BI for analytics and reporting. Transitioning dashboards to Looker while maintaining functionality and ensuring data accuracy required significant effort.
Cost Optimization: A primary goal was to reduce data warehousing costs while maintaining or enhancing performance.
Data Verification: Ensuring the integrity and accuracy of the migrated data was paramount to avoid business disruptions.
Migration Appraoch:
Phase 1: Data Staging and Migration
Data Staging in Cloud Storage:
The migration began by exporting data from Snowflake and staging the Parquet files in Google Cloud Storage (GCS). This approach ensured a secure, scalable, and cost-effective intermediary storage solution.
Loading Data into BigQuery:
Leveraging BigQuery’s bqload utility, the staged data was efficiently ingested into the BigQuery data warehouse. Special attention was given to optimizing schema design to align with BigQuery’s best practices, ensuring performance and cost
Phase 2: ETL Reconfiguration
Redirecting Hevo Streams:
Post bulk migration, the client’s existing Hevo ETL pipelines streaming data from MongoDB to Snowflake were reconfigured to redirect data streams to BigQuery. This involved creating service accounts, reauthorising connections, updating pipeline configurations, and conducting rigorous testing to ensure uninterrupted data flow.
Phase 3: Dashboard Transition
Migrating Dashboards to Looker:
Power BI dashboards were recreated in Looker, preserving functionality and ensuring alignment with existing metrics. BigQuery scheduled queries were utilized to create data marts, simplifying data access and enhancing performance for Looker dashboards.
Functionality Validation:
A thorough comparison of metrics and visualizations between Power BI and Looker dashboards ensured accuracy and consistency.
Phase 4: Cost Optimization and Performance Enhancements
Slot Commitment:
To maximize cost efficiency, the client adopted BigQuery’s slot commitment model. This approach provided dedicated query processing capacity at a discounted rate compared to Snowflake, resulting in significant cost savings.
Performance Monitoring:
Comprehensive performance benchmarks ensured that BigQuery not only matched but exceeded Snowflake’s performance in terms of query execution time and concurrency handling.
Phase 5: Data Verification
Row Count Comparison:
SQL queries were executed in both Snowflake and BigQuery to count rows, ensuring data completeness.
Metric Validation:
Key business metrics were compared between Looker and Power BI dashboards to confirm data accuracy post-migration.
The Result:
Cost Savings:
By transitioning to BigQuery and leveraging slot commitments, the client achieved a 20% reduction in data warehousing costs compared to Snowflake.
Improved Performance:
Query execution times improved significantly, enabling faster insights and decision-making.
Enhanced Integration:
The seamless integration of BigQuery with GCP’s ecosystem streamlined operations and reduced complexity.
Accurate Dashboards:
Migrated Looker dashboards retained all functionalities and metrics, ensuring business continuity.
Scalability:
BigQuery’s serverless architecture provided the flexibility to handle future data growth without additional infrastructure investment.
Conclusion:
Our client’s successful migration from Snowflake to BigQuery underscores the transformative potential of strategic data warehouse transitions. By addressing challenges with meticulous planning and leveraging BigQuery’s capabilities, we delivered a cost-effective, high-performance solution that empowers the client’s data-driven initiatives. For businesses facing similar challenges, our expertise in data migration and cloud optimization can drive impactful results tailored to your unique needs. Contact us to explore how we can help you achieve your data goals.