Optimizing the data warehouse at Visa

At Visa, the process of optimizing the enterprise data warehouse and consolidating data marts by migrating these analytic workloads to Hadoop has played a key role in the adoption of the platform and how data has transformed Visa as an organization. Nandu Jayakumar and Justin Erickson share Visas journey along with some best practices for organizations migrating workloads to Hadoop.

There tends to be a lot of talk around enabling next-generation use cases or advanced analytics for big data and technologies such as Hadoop. While those applications should play a role in any enterprise’s data strategy, much of the initial value and benefits of a modern data platform often comes from simply off-loading the analytic workloads you’re running today. However, where do you start when developing an off-load strategy? Which workloads are better suited for Hadoop? And how do you off-load complex analytic workloads? At Visa, the process of optimizing the enterprise data warehouse and consolidating data marts by migrating these analytic workloads to Hadoop has played a key role in the adoption of the platform and how data has transformed Visa as an organization. Nandu Jayakumar and Justin Erickson share Visa’s journey along with some best practices for organizations migrating workloads to Hadoop. In particular, Nandu and Justin discuss Visa’s approach to bringing Hadoop into its business, its off-load strategy, and the lessons learned from the experience. They also explain how Visa is using new technologies to help better understand the legacy workloads running today and prioritize migrations to continue to alleviate costs and pressures on these systems and and offer a glimpse at what’s in store for the future.