Fermilab Computing Division

Google's next step in Big Data Processing

Full Title: Google's next step in Big Data Processing
Date & Time: 20 Oct 2015 at 15:30
Event Location: Hornet's Nest WH8X
Event Topic(s): Computing Techniques Seminar
Event Moderator(s):
Event Info: Speaker:
Dr. Frances Perry
Software Engineer, Google

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves. On top of that -- consumers want answers now. At Google, we’ve evolved our earlier work on batch and streaming systems (including apReduce, FlumeJava, and Millwheel)into Dataflow, a new programming model that allows users to clearly trade off completeness, latency, and cost. I’ll provide an overview of this model, demo the fully managed service it enables, and discuss some of the many use cases that got us here.

Frances Perry is a software engineer who likes to make big data processing easy, intuitive, and efficient. After many years working on Google's internal data processing stack, she joined the Cloud Dataflow team to make Google’s data processing technology available to external cloud customers. Frances has a BA in Computer Science from Cornell and a PhD in Computer Science from Princeton.

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