000			01584nam a22003137a 4500
999			_c8831 _d8831
001			WIIW0000174
003			OSt
005			20190802144346.0
006			a|||||q|||||00| 0
008			190802t2017 |||||q|||||00| 0 eng d
020			_a1-4919-7295-5
020			_a978-1-4919-7295-3
040			_cOSt
041			_aeng
100	1		_aRyza, Sandy _4aut
245	0	0	_aAdvanced analytics with Spark. _bPatterns for learning from data at scale
250			_aSecond edition
260		1	_aBeijing _bO'Reilly _c[2017]
300			_aXII, 264 S. _bIll.
506			_aLizenzbedingungen können den Zugang einschränken. License restrictions may limit access.
520			_aThe 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.
650			_aData Analyses & Machine Learning
650		0	_aBig data
650		0	_aData mining _xComputer programs
700	1		_aLaserson, Uri _4aut
700	1		_aOwen, Sean _4aut
700	1		_aWills, Josh _4aut
942			_cE