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Advanced analytics with Spark : patterns from learning from data at scale /

The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. You'll start with an introduction to Spark and its eco...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Ryza, Sandy (Autor), Laserson, Uri (Autor), Owen, Sean (Autor), Wills, Josh (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, 2017.
Edición:Second edition.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Advanced analytics with Spark :  |b patterns from learning from data at scale /  |c Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills. 
250 |a Second edition. 
264 1 |a Sebastopol, CA :  |b O'Reilly Media,  |c 2017. 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
588 |a Description based on online resource; title from title page (Safari, viewed June 19, 2017). 
500 |a Previous edition published: 2015. 
500 |a Includes index. 
505 0 |a Analyzing 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. 
520 |a The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
630 0 0 |a Spark (Electronic resource : Apache Software Foundation) 
630 0 7 |a Spark (Electronic resource : Apache Software Foundation)  |2 fast 
650 0 |a Big data. 
650 0 |a Data mining  |x Computer programs. 
650 6 |a Données volumineuses. 
650 6 |a Exploration de données (Informatique)  |x Logiciels. 
650 7 |a Big data  |2 fast 
700 1 |a Laserson, Uri,  |e author. 
700 1 |a Owen, Sean,  |e author. 
700 1 |a Wills, Josh,  |e author. 
776 0 8 |i Print version:  |a Ryza, Sandy.  |t Advanced analytics with Spark : patterns from learning from data at scale.  |b Second edition.  |d Sebastopol, California : O'Reilly Media, 2017  |z 9781491972953 
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