[[{"fid":"613","view_mode":"embedded_left","fields":{"format":"embedded_left","field_file_image_alt_text[und][0][value]":"Patrick Wendell","field_file_image_title_text[und][0][value]":"","field_file_caption_credit[und][0][value]":"","field_file_caption_credit[und][0][format]":"full_html"},"type":"media","link_text":null,"attributes":{"alt":"Patrick Wendell","height":287,"width":250,"class":"media-element file-embedded-left"}}]]This talk will feature Patrick Wendell, class of 2011. Two years after graduating from Princeton in Computer Science, Patrick co-founded Databricks, a company commercializing the Apache Spark software platform for large scale data processing. Databricks helps companies extract value from large amounts of data. Over the last two and a half years, Databricks has raised more than $40 million inventure funding from lead investors Andreessen Horowitz and NEA Ventures. The company employs more than 70 people at its San Francisco headquarters.
In this talk, which will be followed by a "meet-and-greet" session, Patrick will revisit the founding of the company and reflect on lessons learned along the path from Princeton, to Berkeley, and finally to the start-up world. Topics will include fundraising, product strategy, building successful teams, transferring technical innovation into usable products, and perspectives on how the undergraduate life differs from the life of an entrepreneur. He will also discuss different avenues to an entrepreneurial career for Princeton students. The session will be highly interactive and allow for questions from the audience.
Patrick Wendell is a co-founder of Databricks as well as a founding Committer and PMC member of the Apache Spark software project. In the Spark project, Patrick has acted as release manager for several Spark releases, in addition to maintaining several subsystems of Spark's core engine. At Databricks, Patrick directs the company's maintenance and development of Spark.Patrick holds an M.S. in Computer Science at UC Berkeley, where his research focused on low latency scheduling for large scale analytics workloads. He holds a B.S.E in Computer Science from Princeton University.
RSVP for this event to bjarvie@princeton.edu