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Building data flows with Celery and SQLAlchemy
--client
pyconau
--show
pycon_au_2013
--room Tasman_A 2532 --force
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Marks
Author(s):
Roger Barnes
Location
Tasman A
Date
jul Sun 07
Days Raw Files
Start
15:50
First Raw Start
15:39
Duration
0:30:00
Offset
0:10:01
End
16:20
Last Raw End
16:11
Chapters
00:00
Total cuts_time
29 min.
http://2013.pycon-au.org/schedule/30051/view_talk
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Reporting and analysis systems rely on coherent and reliable data, often from disparate sources. To that end, a series of well established data warehousing practices have emerged to extract data and produce a consistent data store. This talk will look at some options for composing workflows using Python. In particular, we'll explore beyond Celery's asynchronous task processing functionality into its workflow (aka Canvas) system and how it can be used in conjunction with SQLAlchemy's architecture to provide the building blocks for data stream processing.
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2013-07-07/15:39:59.dv
Apply:
15:40:39 - 16:10:10 ( 00:29:31 )
S:
15:39:59 -
E:
16:10:10
D:
00:30:11
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Start:
00:00:40)
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vlc ~/Videos/veyepar/pyconau/pycon_au_2013/dv/Tasman_A/2013-07-07/15:39:59.dv :start-time=040.0 --audio-desync=0
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15:39:59
seconds: 40.0
Wall: 15:40:39
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00:30:11
16:10:10
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Wall: 15:39:59
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2013-07-07/16:10:10.dv
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16:10:10 - 16:11:42 ( 00:01:32 )
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16:10:10
seconds: 0.0
Wall: 16:10:10
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16:11:42
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