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Lighting Fast Cluster Computing with PySpark
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continuum
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pydata_sv_2013
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Marks
Author(s):
Patrick Wendell
Location
A1
Date
mar Wed 20
Days Raw Files
Start
16:00
First Raw Start
15:59
Duration
00:50:00
Offset
0:00:22
End
16:50
Last Raw End
16:38
Chapters
00:00
Total cuts_time
30 min.
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This talk will introduce PySpark a framework for cluster scale data-intensive computing. PySpark is a deeply integrated set of Python API bindings for the popular Spark computation engine. Spark provides rich data-flow abstractions and sophisticated use of distributed caching to speed up complex analytic processing by several orders of magnitude. It interfaces directly with popular storage layers (e.g. Hadoop HDFS) and is optimized for advanced analytic functions such as machine learning, OLAP processing, and ETL. The talk will introduce the PySpark API and architecture, present use cases, and walk through a demo.
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2013-03-20/15:59:38.dv
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15:59:38 - 16:30:13 ( 00:30:35 )
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15:59:38
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Wall: 15:59:38
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16:30:13
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2013-03-20/16:30:13.dv
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16:30:13 - 16:38:22 ( 00:08:09 )
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16:30:13
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00:08:09
16:38:22
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Wall: 16:30:13
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2013-03-20/15:59:38.dv
2013-03-20/16:30:13.dv
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