Global Asset Management Firm Optimizes Performance & Reduces Cost by Migrating to Snowflake
Customer's Challenge
A celebrated global asset management firm sought to modernize its data platform to improve performance and reduce the operational costs of its existing SQL Server database system.
phData's Solution
phData proved the value of Snowflake as the new modern data platform by migrating a critical portion of Equities data from SQL Server to Snowflake, showcasing the potential and value of the new platform.Â
Results
The migration to Snowflake not only set the customer up with a modern data platform but also reduced what they had previously been spending on their legacy enterprise data warehouse. phData leaned on thought leadership to drive the platform design and ensured the delivery of a scalable, forward-thinking solution for future use cases.
The Full Story
phData began the engagement with a discovery phase, where, together with the client, we reviewed the current state architecture and requirements documentation. In reviewing the data structures and architecture data in the SQL Server data warehouse, it was discovered that roughly 5,000 tables, 500 views, 1,500 stored procedures, and 200 functions would need to be incorporated into the workload.Â
Additionally, the teams documented existing ETL pipelines, data models, and information architecture that would be impacted. Organizational and development standards were also considered during this phase.Â
During the design phase of the engagement, the teams aligned on the data model, set templates for stored procedures and agreed on CI/CD and source control. The design and architecture were also validated with the customer before moving into the implementation and testing phase.Â
Why phData?
The client knew phData had a lot of experience and success in migrating data to Snowflake and wanted to work with a team of professionals who could accelerate and safeguard the project.
Implementation Begins
The phData team next began implementing data transformations and data models in Snowflake. This step includes developing and testing Snowflake stored procedures and function code for business logic and transformations. Orchestration schedules were also set for stored procedures and functions. phData worked with the customer to test and validate the data models and data transformation pipelines.
Next, both teams focused on documentation and the coordination of knowledge transfer. The documentation included proposed architectures, implementation plans, testing blueprints, test results, and any user stories, backlogs, and epics developed in support of the engagement. phData also provided the customer with scripts, templates, and other automation or code artifacts produced throughout the process.
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