Market place: Bank
Industry: Financial Sector
Task: At the customer data warehouse, the Teradata solution was implemented, further expansion of the storage required significant investments. It was necessary to make a choice between depth of storage and economic efficiency, the transfer of archived data to tape storages made it impossible to use them for advanced analytics.
Solution: It is recommended to use data virtualization and transfer rarely used data and part of the ETL processes into Hadoop.
Architecture of Data Warehouse virtualization solution
Result: The task for cost-effective scaling of storage capacity and compute capacity was solved with ROI > 200% for the first stage and more than 500% for further extension.
Due to data virtualization, a more holistic and flexible work with data has been achieved. The speed of data preparation for analytics has significantly increased, which makes it possible to reduce costs for the preparation and publication of data for analytical tools and applications for up to 75%.