Organizations must rethink their data-management architectures as they embrace Hadoop, protecting their most critical data assets while empowering the business to perform innovative analysis. How can you make sure data doesn’t get lost in Hadoop? How can users across your company find the data they care about and know that it’s trustworthy? How can you protect yourself against a data breach? How can you ingest more and more data while meeting these needs?
Mark Donsky and Chang She explore how some of the world’s most sophisticated Hadoop deployments are addressing these data challenges head-on, while preserving Hadoop’s flexibility, through an integrated data management and governance approach for Hadoop. Employing a live demo and covering audit, lineage, unified metadata, and policy management, as well as leading partner integrations for end-to-end visibility, Mark and Chang discuss how users can discover, trust, protect, and govern the data that matters most for its entire lifecycle.
Mark Donsky leads product management at Okera, a software provider that provides discovery, access control, and governance at scale for today’s modern heterogeneous data environments. Previously, Mark led data management and governance solutions at Cloudera. Mark has held product management roles at companies such as Wily Technology, where he managed the flagship application performance management solution, and Silver Spring Networks, where he managed big data analytics solutions that reduced greenhouse gas emissions by millions of dollars annually. He holds a BS with honors in computer science from the University of Western Ontario.
Chang She is a software engineer on Cloudera Navigator creating metadata management tools for Hadoop. Prior to joining Cloudera, Chang was cofounder and CTO of DataPad, a next-gen BI/analytics company. An early core contributor to the pandas library, Chang’s passion is creating data tools to make people more productive. Chang is a recovering financial quant with bachelor’s and master’s degrees in EECS from MIT.
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