MARC Record from marc_columbia

LEADER: 03764cam a2200601Ii 4500
001 13626013
005 20220528225633.0
006 m o d
007 cr cnu|||unuuu
008 170615s2017 caua o 001 0 eng d
035 $a(OCoLC)ocn990142088
035 $a(NNC)13626013
040 $aN$T$beng$erda$epn$cN$T$dN$T$dEBLCP$dTEFOD$dYDX$dIDEBK$dOCLCQ$dALAUL$dCOO$dHCO$dOCLCF$dOCLCO$dORU$dZCU$dUKAHL$dOCLCQ$dOCLCO
019 $a990316196
020 $a9781491972922$q(electronic bk.)
020 $a1491972920$q(electronic bk.)
020 $a9781491972908$q(electronic bk.)
020 $a1491972904$q(electronic bk.)
020 $z9781491972953
020 $z1491972955
035 $a(OCoLC)990142088$z(OCoLC)990316196
037 $a2E396982-C83B-4056-B7D6-D56AAC6C25BC$bOverDrive, Inc.$nhttp://www.overdrive.com
050 4 $aQA76.9.D343$bR93 2017eb
072 7 $aCOM$x000000$2bisacsh
082 04 $a006.312$223
049 $aZCUA
100 1 $aRyza, Sandy,$eauthor.
245 10 $aAdvanced analytics with Spark :$bpatterns for learning from data at scale /$cSandy Ryza, Uri Laserson, Sean Owen and Josh Wills.
250 $aSecond edition.
264 1 $aSebastopol, CA :$bO'Reilly Media,$c[2017]
300 $a1 online resource (xii, 260 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
500 $aIncludes index.
588 0 $aOnline resource; title from PDF title page (EBSCO, viewed June 20, 2017).
520 $a"In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--to fields such as genomics, security, and finance."--Provided by publisher.
505 0 $aAnalyzing big data -- Introduction to data analysis with Scala and Spark -- Recommending music and the audioscrobbler data set -- Predicting forest cover with decision trees -- Anomaly detection in network traffic with K-means clustering -- Understanding Wikipedia with latent semantic analysis -- Analyzing co-occurrence networks with GraphX -- Geospatial and temporal data analysis on the New York City taxi trip data -- Estimating financial risk through Monte Carlo simulation -- Analyzing genomics data and the BDG project -- Analyzing neuroimaging data with PySpark and Thunder.
630 00 $aSpark (Electronic resource : Apache Software Foundation)
630 07 $aSpark (Electronic resource : Apache Software Foundation)$2fast$0(OCoLC)fst01938143
650 0 $aBig data.
650 0 $aData mining$xComputer programs.
650 6 $aDonnées volumineuses.
650 6 $aExploration de données (Informatique)$xLogiciels.
650 7 $aCOMPUTERS$xGeneral.$2bisacsh
650 7 $aBig data.$2fast$0(OCoLC)fst01892965
655 4 $aElectronic books.
700 1 $aLaserson, Uri,$d1983-$eauthor.
700 1 $aOwen, Sean,$eauthor.
700 1 $aWills, Josh,$eauthor.
776 08 $iPrint version:$aRyza, Sandy.$tAdvanced analytics with Spark.$bSecond edition.$dSebastopol, CA : O'Reilly Media, [2017]$z1491972955$z9781491972953$w(OCoLC)959596273
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13626013$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS