Overview
Our client, a leading US solar services provider enhanced their data ecosystem by switching from Snowflake to Google BigQuery for improved scalability, performance, and centralized access. Over six months, 2000+ tables and 100+ procedures were migrated, overcoming challenges such as text handling and tooling limits. Robust ETL workflows and validations provided a smooth, timely transition.
Customer Challenges
The migration from Snowflake to BigQuery posed several hurdles due to the scale and complexity of the data environment.
Centralizing and Migrating Complex Data
Snowflake included datasets from Salesforce, NetSuite, and analytics, making it difficult to centralize and migrate these diverse data sources. The approach required precise management to guarantee data integrity and analytical functionality following the migration.
Text Column Handling
Informatica Intelligent Cloud Services (IICS) defaulted to STRING(MAX) for text columns in the target schema, leading to storage and query performance inefficiencies.
Tooling Limitations
IICS Mass Ingestion lacked native support for Snowflake as a source, creating a significant hurdle in the migration process. This limitation required finding alternative solutions to ensure seamless data extraction and integration.
Handling Big Data and Complex Tables
Migrating tables with over 100 million rows and more than 500 columns required effective partitioning to avoid performance bottlenecks.
Developing a Consistent Naming Convention
Aligning Snowflake table names with BigQuery’s best practices presented the challenge of maintaining consistency while ensuring traceability. The naming convention had to be carefully developed to meet BigQuery’s requirements without losing reference to the original structure.
Managing and Preserving Unused Scripts
With over 200 procedures and views in Snowflake, many of which were obsolete, the challenge was ensuring that these scripts were properly preserved for future reference without complicating the migration process or affecting system performance.
BigQuery Migration Architecture from Snowflake
ETL pipelines migrate Snowflake datasets into Google BigQuery, enabling scalable storage, optimized queries, and centralized analytics.
Solutions
To address the problems of migrating from Snowflake to BigQuery, NeenOpal provided a variety of tailored solutions to ensure a smooth transition, improve performance, and protect data integrity. These methods aimed to improve data management, maintain compatibility, and streamline the migration process.
01.
Optimizing Data Pipelines for BigQuery Migration
ETL workflows were established to extract data from Snowflake while ensuring analytics pipelines were either adapted or re-engineered for BigQuery’s architecture.
02.
Refining String Column Mappings
Customized mappings in IICS limited string column lengths (e.g., 10,000 characters) for optimized storage and improved performance in BigQuery.
03.
Ensuring Efficient Migration with Informatica CDI
Informatica CDI jobs were leveraged to ensure compatibility and support efficient migration workflows
04.
Enhancing Data Loads and Incremental Migrations
To handle large datasets efficiently, partitioned data loads were implemented alongside tested incremental migrations. This approach ensured that data was processed in manageable chunks, preventing performance bottlenecks and enabling smooth transitions during the migration process.
05.
Implementing a Consistent Naming Convention
A naming convention was implemented to transform Snowflake table names into BigQuery-friendly formats, embedding original database and schema names for consistency.
Transform Your Data Ecosystem with Scalable, High-Performance BigQuery Solutions
Consult Our Data ExpertsServices
Benefits
Improved Performance
Leveraged BigQuery’s serverless architecture for enhanced query speeds and reduced resource management overhead.
Unified Data Ecosystem
Centralized Salesforce, NetSuite, and analytics datasets in BigQuery, enabling a single source of truth for reporting and analytics.
Scalability for Analytics
Migrated analytics datasets to a platform capable of handling large-scale data more efficiently.
On-Time Delivery
Completed the migration within six months, with no operational disruptions.
Data Integrity
Preserved the integrity of critical datasets and ensured historical metadata remained accessible in BigQuery.
Conclusion
The migration empowered the client with a unified, high-performing data ecosystem that supports advanced analytics and reporting. By transitioning to BigQuery, the organization is now positioned for future scalability, reduced operational overhead, and improved decision-making capabilities, laying the foundation for data-driven growth and innovation.
FAQ
Find answers to common questions about Snowflake to BigQuery migration, challenges, timelines, tools, and business impact.
Why did the client migrate from Snowflake to Google BigQuery?
The client wanted improved scalability, faster query performance, and a centralized analytics platform. BigQuery’s serverless architecture reduced operational overhead while supporting large-scale data processing more efficiently.
How complex was the migration process?
The migration involved 2000+ tables and 100+ procedures over six months. Challenges included handling large datasets (100M+ rows), text column optimization, tooling limitations, and maintaining data integrity throughout the transition.
How were large tables and big data handled during migration?
Partitioned data loads and incremental migration strategies were implemented to process data in manageable chunks. This prevented performance bottlenecks and ensured a smooth transition.
Authors
Contact Us
We’d love to hear from you.
Lets discuss how we can transform your business with AI. Talk to our AI expert team. Lets do AI journey together.
