Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Optimizing the data warehouse at Visa

2:05pm2:45pm Wednesday, September 27, 2017
Enterprise adoption, Strata Business Summit
Location: 1A 18 Level: Intermediate
Secondary topics:  Financial services, Platform
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Who is this presentation for?

  • Architects, IT managers, and those in the C-suite

Prerequisite knowledge

  • A general understanding of traditional data warehousing, big data platforms, and BI (and how they are implemented at enterprises)
  • Familiarity with SQL and the workflows of typical data analysts at enterprises

What you'll learn

  • Gain insight into how Visa is optimizing its data warehouse landscape with Hadoop
  • Understand how to develop an off-load strategy and best practices for off-loads


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.

Photo of Nanda Kumar Jayakumar

Nanda Kumar Jayakumar


Nandu Jayakumar is a software architect and engineering leader at Visa, where he is currently responsible for the long-term architecture of data systems and leads the data platform development organization. Previously, as a senior leader of Yahoo’s well-regarded data team, Nandu built key pieces of Yahoo’s data processing tools and platforms over several iterations, which were used to improve user engagement on Yahoo websites and mobile apps. He also designed large-scale advertising systems and contributed code to Shark (SQL on Spark) during his time there. Nandu holds a bachelor’s degree in electronics engineering from Bangalore University and a master’s degree in computer science from Stanford University, where he focused on databases and distributed systems.

Photo of Justin Erickson

Justin Erickson


Justin Erickson is a senior director of product management leading Cloudera’s platform team, which is responsible for the components in Cloudera Distribution, including Hadoop (CDH) above storage. Previously, he led the high-availability and disaster-recovery areas of Microsoft SQL Server.