The cloud has the potential to deliver on the promise of big data processing for machine learning and analytics to help organizations become more data-driven, however, it presents its own set of challenges. This webinar covers best practices in areas such as. - Using automation in the cloud to derive more value from big data by delivering self-service access to data lakes for machine learning and analytics - Enabling collaboration among data engineers, data scientists, and analysts for end-to-end data processing - Implementing financial governance to ensure a sustainable program - Managing security and compliance - Realizing business value through more users and use cases In addition, this webinar provides an overview of Qubole’s cloud-native data platform’s capabilities in areas described above. About Our Speaker: James Curtis is a Senior Analyst for the Data, AI & Analytics Channel at 451 Research. He has had experience covering the BI reporting and analytics sector and currently covers Hadoop, NoSQL and related analytic and operational database technologies. James has over 20 years' experience in the IT and technology industry, serving in a number of senior roles in marketing and communications, touching a broad range of technologies. At iQor, he served as a VP for an upstart analytics group, overseeing marketing for custom, advanced analytic solutions. He also worked at Netezza and later at IBM, where he was a senior product marketing manager with responsibility for Hadoop and big data products. In addition, James has worked at Hewlett-Packard managing global programs and as a case editor at Harvard Business School. James holds a bachelor's degree in English from Utah State University, a master's degree in writing from Northeastern University in Boston, and an MBA from Texas A&M University.

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