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Private and Open Data in Asia: A Regional Guide.
Many of the common data analysis methods are expensive to scale to big datasets. In this talk, we introduce a recent effort in Spark to employ randomized algorithms for a number of common, expensive methods: membership testing, cardinality, stratified sampling, frequent items, quantile estimation. We will discuss the algorithms of choice and their implementation in Spark.
Reynold Xin is a cofounder and chief architect at Databricks as well as an Apache Spark PMC member and release manager for Spark’s 2.0 release. Prior to Databricks, Reynold was pursuing a PhD at the UC Berkeley AMPLab, where he worked on large-scale data processing.
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