International Retail Apparel Company: Optimized Teradata to Snowflake Migration


Our client is a well-recognized retail apparel chain headquartered in Texas and operating 1,500+ stores in the US and Canada. The company has acquired many big market players as its subsidiaries. The company offers men’s and women’s clothing, footwear, tuxedo rentals, and suit pressing with quality, fashion, and innovation as a central part of each product. The company has many warehouse facilities with a reliable supply chain mechanism. Globally, around 22,500 employees are providing hightouch, high-quality shopping experience across their brands.


Moving to the cloud was one of the major parts of the client’s technology transformation. The main goal of the transformation initiative was to improve data availability, elastic scalability, and reduce IT operational costs. Coincidentally, their Teradata data warehouse platform was close to its 100% utilization and it was reaching its end-of-life. Adding more Teradata nodes on-premise meant additional cost and the only option available was to move to the cloud as part of the technology transformation initiative.

The decision to migrate Teradata data warehouse to Snowflake Cloud Data Platform came out of an analysis based on multiple factors such as cost-benefit, ease of workload management, ease of migration, elastic scalability, learning curve, ease of use, and ease of replication and recovery.


With two decades of experience in data warehouse implementations and migrations, XTIVIA played a significant role in this forklift migration from Teradata to Snowflake. As part of a multi-vendor team, XTIVIA utilized its data warehouse migration framework and data validation framework to accomplish a smooth migration within the estimated schedule.

  • Performed initial data load from Teradata to Snowflake
  • Migrated DataStage jobs from Teradata to Snowflake
  • Modified existing tool (Groovy SQL code generator) to generate Snowflake ETL SQL
  • Compared Snowflake SQL code with existing
  • Validated and provided feedback to other vendors on the code they generated
  • Scheduled the Snowflake ETLs with the UC4 scheduler and executed them in parallel with existing Teradata UC4 jobs
  • Performed parallel load test and data sync between Teradata and Snowflake
  • Monitored Snowflake performance with existing Teradata workloads and assisted with tuning where necessary
  • Assisted in validating historical data between Teradata and Snowflake
  • Reviewed the test cases, and test results from other vendors and provided feedback
  • Deployed the Snowflake code (DDLs, ETL, UC4 Objects, UC4 Schedules) in UAT environment
  • Monitored Snowflake daily UAT loads and resolved issues
  • Fixed UAT data issues and coordinate with other vendors to fix major code issues
  • Migrated the code (DDLs, ETL, UC4 Objects, UC4 Schedules) to the production environment
  • Monitored production loads (Snowflake and existing Teradata in parallel), explained the differences if any, and fixed minor data sync issues
  • Developed additional ETLs into new summary tables in Snowflake to migrate complex transformation logic from Micro-Strategy to Snowflake

Migration to Snowflake

Business Result

XTIVIA’s robust solution was able to solve the various integration problems our client was facing and improve the overall IT infrastructure with the following business results:

  • Stable data warehouse platform
  • Highly performant ETL flow
  • Improved workload and compute resource management leading to better data availability and accessibility
  • Reduction in administration costs (no need to worry about query tuning, data distribution, collection of statistics, indexes, maintenance windows, disk failure issues, patching, upgrades, close monitoring of capacity utilization, reviewing reports, workload analysis, capacity planning, etc.)
  • Reduced storage and compute costs (pay for computing only for the time used)
  • Visibility into cost and resource utilization
  • Elastic scalability and auto-suspend of computing resources based on rules
  • Zero-copy cloning of production data to non-production environments

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