The client was a leading financial group with a variety of services from banking who wished to migrate from an on-premise solution to a cloud data warehouse.
About the Client
Problem Statement
The client wants a performant data warehouse solution that has to be flexible for new requirements. In the existing data warehouse solution, we had several issues regarding this:
- Limited scalability and performance constraints of an existing data warehouse
- Inflexible architecture inhibits agility and responsiveness to handle the business requirements
- No pipelines for data quality to ensure data accuracy for analysis
- High maintenance costs and no cost management
The Challenge
A migration to a cloud data warehouse can lead to several problems. There are multiple factors which decides about the success of the migration:
- Team training for new tools and Data Vault 2.0
- Seamless communication and coordination of cross-functional teams to mitigate risks and handle tight timelines
- Compatibility from existing solution with new cloud data warehouse solutions
The Solution
In order to fulfill the requirements for a successful migration the architecture changed. The implementation of Data Vault 2.0, in combination with dbt and Snowflake, provided flexibility and scalability for the new cloud data warehouse.
The transformation tool dbt with the open-source package DataVault4dbt simplified the data vault implementation and had a high customizability for individual requirements. In addition, dbt provides generic and custom tests to ensure high data quality.
The transformation tool dbt with the open-source package DataVault4dbt simplified the data vault implementation and had a high customizability for individual requirements. In addition, dbt provides generic and custom tests to ensure high data quality.
Tangible Results for the Client
The migration to Snowflake and adoption of Data Vault 2.0 methodology resulted in streamlined operations and accelerated insights delivery. This led to optimized costs and improved agility, empowering the client to make data-driven decisions efficiently, ultimately driving business growth and competitiveness.
Technologies involved
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