To view just the On Demand recording of this presentation, click HERE>>
This webinar is sponsored by:
About the Webinar
Greater agility, scalability, and lower total cost of ownership made the decision to move key elements of your organization’s data capability to the cloud easy. The real challenge is migrating data from your legacy systems to your new cloud platform so you can unleash its potential and value while minimizing the migration risks.
Combining erwin‘s data modeling, governance, and intelligence solutions with Snowflake’s modern cloud data platform, organizations can realize a scalable, governed, and transparent enterprise data capability.
In this session, we’ll show you how enterprise stakeholders with different skills and needs can work together to accelerate and assure the success of cloud migration projects of any size. You’ll learn how to:
- Reduce costs and mitigate risks when migrating legacy applications to Snowflake with erwin’s model-driven schema design and transformation capabilities
- Increase the precision, speed, and agility of Snowflake deployments with erwin data automation
- Assure transparency, compliance, and governance for Snowflake data and processes
- Increase the efficiency and accuracy of analytics and other data usage on the Snowflake Cloud Platform
About the Speaker
Director of Product Marketing, erwin
Danny Sandwell is an IT industry veteran with more than 30 years of experience. As Director of Product Marketing for erwin, he is responsible for communicating the technical capabilities and business value of the company’s data modeling and data intelligence solutions. During Danny’s 20+ years with the company, he also has worked in pre-sales consulting, product management, business development, and business strategy roles – all giving him opportunities to engage with customers across various industries as they plan, develop, and manage their data architectures. His goal is to help enterprises unlock the value of their data assets to produce the desired results while mitigating data-related risks.