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    <title>Pinboard (amcclosky)</title>
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    <description>recent bookmarks from amcclosky</description>
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	<rdf:li rdf:resource="http://misoproject.com/dataset/"/>
	<rdf:li rdf:resource="http://www.geonames.org/"/>
	<rdf:li rdf:resource="http://www.10gen.com/presentations/mongosv-2011/theres-a-monster-in-my-closet-architecture-of-a-mongodb-powered-event-processing-system"/>
	<rdf:li rdf:resource="http://www.cs.cmu.edu/~dga/papers/cops-sosp2011.pdf"/>
	<rdf:li rdf:resource="https://bitbucket.org/zzzeek/alembic"/>
	<rdf:li rdf:resource="http://bret.appspot.com/entry/how-friendfeed-uses-mysql"/>
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  </channel><item rdf:about="http://heynemann.github.com/r3/">
    <title>R3 by heynemann</title>
    <dc:date>2012-07-19T18:34:20+00:00</dc:date>
    <link>http://heynemann.github.com/r3/</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA[r³ is a map reduce engine written in python using a redis backend. It's purpose is to be simple.]]></description>
<dc:subject>python data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:263b7bb417eb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:python"/>
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<item rdf:about="http://www.youtube.com/watch?v=F7iopLnhDik">
    <title>&quot;The Model and the Train Wreck: A Training Data How-To&quot; -- Monica Rogati @ O'Reilly Strata 2012 - YouTube</title>
    <dc:date>2012-06-05T22:25:43+00:00</dc:date>
    <link>http://www.youtube.com/watch?v=F7iopLnhDik</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["'More data beats clever algorithms, but better data beats more data.'
LinkedIn Data Scientist Monica Rogati (@mrogati) talks about the art of obtaining good training data and why it matters.
Courtesy of O'Reilly Strata 2012"]]></description>
<dc:subject>data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:7307624aaec0/</dc:identifier>
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<item rdf:about="http://www.cs.rmit.edu.au/swirl12/discussion.php">
    <title>SWIRL 2012 Pre-workshop discussion</title>
    <dc:date>2012-06-05T22:23:31+00:00</dc:date>
    <link>http://www.cs.rmit.edu.au/swirl12/discussion.php</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["SWIRL participants were asked to nominate three papers that, in their opinion, represent important new directions, research areas, or results in the IR field."]]></description>
<dc:subject>data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:901b645c0d67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:data"/>
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<item rdf:about="http://www.youtube.com/watch?v=2SQ0O_oPpe4">
    <title>1M. 10M. 100M. Data! LinkedIn Data Scientist Monica Rogati's talk at O'Reilly Strata 2011 - YouTube</title>
    <dc:date>2012-06-05T22:20:51+00:00</dc:date>
    <link>http://www.youtube.com/watch?v=2SQ0O_oPpe4</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["How do data infrastructure, insights and products change when your user base grows by orders of magnitude? When should you move your user-facing data product off your laptop? (hint: now!) Does your data offer insights about the world at large, or is it just mirroring your early adopters? In this talk, I will share some of the data scaling lessons we've learned at LinkedIn, recount war stories (and close calls!) and document the evolution of the data scientist."]]></description>
<dc:subject>data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:9c592dd1baaf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:data"/>
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<item rdf:about="http://flowingdata.com/2012/04/27/data-and-visualization-blogs-worth-following/">
    <title>Data and visualization blogs worth following</title>
    <dc:date>2012-04-27T17:06:25+00:00</dc:date>
    <link>http://flowingdata.com/2012/04/27/data-and-visualization-blogs-worth-following/</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["this list is restricted to blogs that have updated in the past two months and are at least four months old."]]></description>
<dc:subject>data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:dcf6e4f6fea3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:data"/>
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</item>
<item rdf:about="http://postgresguide.com/">
    <title>Postgres Guide — Postgres Guide</title>
    <dc:date>2012-04-24T22:30:29+00:00</dc:date>
    <link>http://postgresguide.com/</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA[Postgres Guide is intended to highlight best practices and great features that exist within Postgres.]]></description>
<dc:subject>postgresql data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:d499ab740821/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:postgresql"/>
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</item>
<item rdf:about="http://blog.avadis-ngs.com/2012/04/elegant-exact-string-match-using-bwt-2/">
    <title>Elegant exact string match using BWT | Read Through Transcription</title>
    <dc:date>2012-04-24T14:39:18+00:00</dc:date>
    <link>http://blog.avadis-ngs.com/2012/04/elegant-exact-string-match-using-bwt-2/</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["This post describes an elegant and fast algorithm to perform exact string match."]]></description>
<dc:subject>algorithms data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:14dd17ab4e95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:algorithms"/>
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</item>
<item rdf:about="http://misoproject.com/dataset/">
    <title>The Miso Project :: Dataset</title>
    <dc:date>2012-04-24T14:34:58+00:00</dc:date>
    <link>http://misoproject.com/dataset/</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["Dataset is a JavaScript client-side data transformation and management library. Dataset makes managing client-side data easy by handling loading, parsing, sorting, querying & manipulating data from all sorts of sources."]]></description>
<dc:subject>javascript data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:fc6555304fc8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:data"/>
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</item>
<item rdf:about="http://www.geonames.org/">
    <title>GeoNames</title>
    <dc:date>2012-01-29T22:52:59+00:00</dc:date>
    <link>http://www.geonames.org/</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["The GeoNames geographical database covers all countries and contains over eight million placenames that are available for download free of charge."]]></description>
<dc:subject>data api</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:bb12ac92dbc5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:data"/>
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<item rdf:about="http://www.10gen.com/presentations/mongosv-2011/theres-a-monster-in-my-closet-architecture-of-a-mongodb-powered-event-processing-system">
    <title>10gen - MongoDB Presentations - There's a Monster in My Closet: Architecture of a MongoDB-powered Event Processing System</title>
    <dc:date>2011-12-27T03:17:24+00:00</dc:date>
    <link>http://www.10gen.com/presentations/mongosv-2011/theres-a-monster-in-my-closet-architecture-of-a-mongodb-powered-event-processing-system</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["Monster is Stripe's in-house framework for producing and consuming events. Whenever a user logs in, whenever a payment is received, whenever a cron job runs, an event is logged into our MongoDB event store. These events update aggregate totals, feed into fraud algorithms, and can be analyzed as a changelog of the system. In this talk, we'll discuss how MongoDB's unique features make it easy to implement Monster."]]></description>
<dc:subject>data mongodb</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:af6772664b68/</dc:identifier>
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<item rdf:about="http://www.cs.cmu.edu/~dga/papers/cops-sosp2011.pdf">
    <title>Don’t Settle for Eventual: Scalable Causal Consistency for Wide-Area Storage with COPS</title>
    <dc:date>2011-12-24T16:21:35+00:00</dc:date>
    <link>http://www.cs.cmu.edu/~dga/papers/cops-sosp2011.pdf</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["In this paper, we identify and deﬁne a consistency model—causal consistency with convergent conﬂict handling, or causal+—that is the strongest achieved under these constraints."]]></description>
<dc:subject>data nosql</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:8b231c32df4e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:data"/>
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</item>
<item rdf:about="https://bitbucket.org/zzzeek/alembic">
    <title>zzzeek / alembic / overview — Bitbucket</title>
    <dc:date>2011-12-16T17:29:36+00:00</dc:date>
    <link>https://bitbucket.org/zzzeek/alembic</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["a database migrations tool for SQLAlchemy."]]></description>
<dc:subject>python data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:804e9d072325/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amcclosky/t:python"/>
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</item>
<item rdf:about="http://bret.appspot.com/entry/how-friendfeed-uses-mysql">
    <title>How FriendFeed uses MySQL to store schema-less data - Bret Taylor's blog</title>
    <dc:date>2011-12-12T03:14:29+00:00</dc:date>
    <link>http://bret.appspot.com/entry/how-friendfeed-uses-mysql</link>
    <dc:creator>amcclosky</dc:creator><description><![CDATA["As our database has grown, we have tried to iteratively deal with the scaling issues that come with rapid growth. We did the typical things, like using read slaves and memcache to increase read throughput and sharding our database to improve write throughput. However, as we grew, scaling our existing features to accomodate more traffic turned out to be much less of an issue than adding new features."]]></description>
<dc:subject>mysql data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amcclosky/b:4a9c851c0165/</dc:identifier>
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