Data Warehouse Modernization using Data Virtualization

Within many organizations, data warehouses have ruled the roots at the center of the reporting and analytics functions. However, many data warehouses have been around for ten or more years and, as such, they have become dated. The core data model at the heart of the data warehouse was likely designed years ago to meet the needs of the business at that time. It will have been modified and extended over the years to the point that it more resembles the 'Winchester Mystery House' rather than a clean data model. The data warehouse will have become rigid and harder and harder to use, the data loading times will have become longer, and user frustration will be mounting.

It's been this way for many years but, to quote Bob Dylan, the times they are a-changin'. Cloud-based data warehouses, such as Snowflake and Yellowbrick, have introduced elastic scalability coupled with usage-based pricing models that have accelerated the drive towards data warehouse modernization. However, this modernization is not without its risks. Data warehouses are used by hundreds or even thousands of reports, dashboards, and other applications as the source of analytical data.

The impact of a data warehouse modernization project can be very large and the risks can be significant. Data Virtualization can minimize the impact of data warehouse modernization and lessen the risks. It can also accelerate the time-to-value from the modernization project and provide an easy-to-follow template for a phased approach to moving your data warehouse to the Cloud.

Attend this webinar to learn:
1. The challenges with data warehouse modernization projects
2. How Data Virtualization can minimize the impact and lessen the risk of these projects
3. How Vancouver Coastal Health use Data Virtualization in their data warehouse modernization project