Tiger Analytics spearheads migrations to Azure and Snowflake
Organizations need to adopt more flexible, scalable, and cost-effective solutions to keep up with the evolving landscape of data analytics. Traditional on-premises systems, while robust and reliable, often face limitations in handling the increasing volume and velocity of data. To thrive, organizations must modernize their critical applications, replacing legacy systems with cloud-native solutions that offer better scalability, fault tolerance, and security.
Microsoft partner Tiger Analytics is at the forefront of this modernization process. The organization has all three Solutions Partner designations for Azure, as well as three specializations: AI Platform on Microsoft Azure (formerly AI and Machine Learning on Microsoft Azure), Build AI Apps on Microsoft Azure, and Analytics on Microsoft Azure. Tiger Analytics used their expertise to execute a digital transformation for a leading global intimates and sleepwear retailer’s analytics ecosystem, delivering a seamless migration from the on-premises model to Azure and Snowflake.
Over a period of 10 months, the intimates retailer was able to transition out of legacy on-premises systems with a 100% success rate on all workloads—and significantly reduced costs along the way. The framework Tiger Analytics used to execute this migration can be tweaked for any organization based on their priorities, processes, and current ecosystem.
Collaborating for a smooth migration
The global intimates and sleepwear retailer is on a mission to build a modern ecosystem that will improve customer experience and enhance productivity. The company adopted Microsoft Azure in 2022 and migrated their enterprise data from Teradata to Snowflake in 2023. With the right foundation in place, the retailer seized the opportunity to build a modern data platform and shed the legacy technology debt.
The retailer collaborated with Tiger Analytics to implement a well-structured, phased migration strategy to ensure a smooth transition of analytical and reporting workloads. The objective of this exercise was not only to refactor the code to a new platform, but also to introduce best practices and efficiencies to improve current processes—all while minimizing effort and costs.
For Tiger Analytics, the first step in the migration strategy was a comprehensive workload prioritization and business impact analysis. This process involved collaboration with business stakeholders, IT, and the data science team to thoroughly understand the current landscape. Their top considerations were the number of scripts per module, need for automation, module dependencies, impact on daily tasks, and complexity of implementation.
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