Resources / Case Studies / Netezza to Snowflake Migration Case Study

Netezza to Snowflake Migration Case Study

This case study demonstrates how a major client successfully migrated their data warehouse from Netezza to Snowflake using iceDQ’s Data Testing and Monitoring Platform, significantly improving the efficiency and accuracy of the migration process. For a comprehensive understanding on migrating and testing your data warehouse to Snowflake, visit our dedicated resource: Snowflake Migration Testing

Key Highlights:

1. Challenge: Previous manual migration experience (Oracle 9 to 12) took 1.5 years and resulted in numerous production defects.

2. Scope: 17 terabyte data warehouse with 5,000 objects (4,000 tables, 1,000 views) across 20 subject areas.

3. Solution: Implementing automated data testing and monitoring with iceDQ.

4. Focus: Ensuring data accuracy, data reliability and reducing migration time.

Essential Metrics:

1. Data Volume: Largest table contained about 100 million records.

2. Test Cases: Approximately 15,000 rules/test cases auto-generated using iceDQ’s bulk rule generation feature.

3. Defect Detection: About 7% of differences in data found between Netezza and Snowflake during testing.

4. Precision: Data validated to the penny and to the 10th decimal place.

Key Benefits:

1. Time Efficiency: Significantly reduced migration time compared to previous manual process.

2. Accuracy: Comprehensive testing of every object at record and attribute level.

3. Automation: Auto-generation of test cases drastically reduced manual effort.

4. Defect Identification: Easy pinpointing of issues like data type differences, timestamp differences, and code conversion errors.

5. Regression Testing: Reusable test cases for incremental loads and future enhancements.

Implementation Highlights:

1. Schema Comparison: Used iceDQ to ensure proper object/datatype migration from Netezza to Snowflake.

2. Automated Rule Generation: Utilized iceDQ’s bulk rule generation to automatically create 15,000 database migration testing rules.

3. Baseline and Incremental Testing: Executed test cases for both initial data load and subsequent incremental loads.

4. Real-time Processing: iceDQ’s in-memory engine eliminated the need for temporary staging databases.

5. Cloud Compatibility: Leveraged iceDQ’s pre-built connectors for cloud services like Salesforce.

This case study illustrates how implementing automated data testing tools can significantly enhance the efficiency, accuracy, and speed of large-scale data warehouse migrations, while also setting the foundation for ongoing DataOps practices.

Download the full case study to learn more about:

1. The critical role of automated data testing in ensuring successful large-scale data warehouse migrations.

2. Strategies for implementing DataOps principles in your organization post-migration.

3. How iceDQ can help you achieve a seamless, efficient, and accurate data warehouse migration.