Video Description
Analyzing real-time data poses special kinds of challenges, such
as dealing with large event rates, aggregating activities for
millions of objects in parallel, and processing queries with
subsecond latency. In addition, the set of available tools and
approaches to deal with streaming data is currently highly
fragmented.
In this webcast, Mikio Braun will discuss building reliable
and efficient solutions for real-time data analysis, including
approaches that rely on scaling--both batch-oriented (such as
MapReduce), and stream-oriented (such as Apache Storm and Apache
Spark). He will also focus on use of approximative algorithms (used
heavily in streamdrill) for counting, trending, and outlier
detection.