Cloudian’s recent product innovation is revolutionizing leading-edge petabyte scale computing in the enterprise. Join Gary Ogasawara, Vice President of Engineering at Cloudian, on November 17th for a technical discussion on extensions to the S3 API to make it an easy-to-use, easy-to-manage platform for enterprise object storage. Amazon S3 API compatibility ensures full portability of already working applications. Using Cloudian’s HyperStore platform instead of AWS, enterprise data can be brought on-premise for better data security and manageability at lower cost. For STaaS providers, S3 API compatibility provides the same benefits of a fully controlled storage platform, and opens up a large range of compatible applications. Beyond the basic object CRUD operations provided by S3, there are many advanced APIs like versioning, multi-part upload, access control list. And beyond the S3 API, Cloudian is committed to providing all operations by API and has added APIs to make the platform enterprise-ready, including multi-tenancy.

Webinar discussion topics will include:

  • Why the S3 API Matters
  • S3 API advanced features
  • Extending the functionality of the S3 API to make an enterprise-ready platform
  • Administrative API extensions



Gary Ogasawara, Vice President of Engineering at Cloudian

Gary Ogasawara heads up Cloudian’s global development and QA team, with operations in Silicon Valley, Tokyo, and Beijing. Gary has over 15 years of hands on engineering management experience at startups and larger companies. Gary has led the development of all of Cloudian’s products and their worldwide carrier deployments. Prior to Cloudian, Gary was Director of Engineering at e-Centives, an e-Commerce search engine company. He also led the development of real time commerce and advertising systems at Inktomi, a leading Internet search engine and CDN gateway company. Gary holds a Ph.D. in Computer Science from the University of California at Berkeley, where he specialized in Bayesian decision theory, uncertainty reasoning and machine learning.