Practical data science : a guide to building the technology stack for turning data lakes into business assets

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Practical data science a guide to building the technology stack for turning data lakes into business assets
verantwortlich
Vermeulen, Andreas François (VerfasserIn)
veröffentlicht
Berkeley, CA: Apress, 2018
©2018
Erscheinungsjahr
2018
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E-Book
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British Library Catalogue
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245 1 0 |a Practical data science  |b a guide to building the technology stack for turning data lakes into business assets  |c Andreas François Vermeulen 
264 1 |a Berkeley, CA  |b Apress  |c 2018 
264 4 |c ©2018 
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500 |a Includes index. 
505 0 |a Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Science Technology Stack; Rapid Information Factory Ecosystem; Data Science Storage Tools; Schema-on-Write and Schema-on-Read; Schema-on-Write Ecosystems; Schema-on-Read Ecosystems; Data Lake; Data Vault; Hubs; Links; Satellites; Data Warehouse Bus Matrix; Data Science Processing Tools; Spark; Spark Core; Spark SQL; Spark Streaming; MLlib Machine Learning Library; GraphX; Mesos; Akka; Cassandra; Kafka; Kafka Core; Kafka Streams; Kafka Connect; Elastic Search; R; Scala. 
505 8 |a PythonMQTT (MQ Telemetry Transport); Whatâ#x80;#x99;s Next?; Chapter 2: Vermeulen-Krennwallner-Hillman-Clark; Windows; Linux; Itâ#x80;#x99;s Now Time to Meet Your Customer; Vermeulen PLC; Krennwallner AG; Hillman Ltd; Clark Ltd; Processing Ecosystem; Scala; Apache Spark; Apache Mesos; Akka; Apache Cassandra; Kafka; Message Queue Telemetry Transport; Example Ecosystem; Python; Ubuntu; CentOS/RHEL; Windows; Is Python3 Ready?; Python Libraries; Pandas; Ubuntu; Centos/RHEL; PIP; Matplotlib; Ubuntu; CentOS/RHEL; PIP; NumPy; SymPy; Scikit-Learn; R; Ubuntu; CentOS/RHEL; Windows; Development Environment; R Studio. 
505 8 |a UbuntuCentOS/RHEL; Windows; R Packages; Data.Table Package; ReadR Package; JSONLite Package; Ggplot2 Package; Amalgamation of R with Spark; Sample Data; IP Addresses Data Sets; Customer Data Sets; Logistics Data Sets; Post Codes; Warehouse Data Set; Shop Data Set; Exchange Rate Data Set; Profit-and-Loss Statement Data Set; Summary; Chapter 3: Layered Framework; Definition of Data Science Framework; Cross-Industry Standard Process for Data Mining (CRISP-DM); Business Understanding; Data Understanding; Data Preparation; Modeling; Evaluation; Deployment. 
505 8 |a Homogeneous Ontology for Recursive Uniform SchemaThe Top Layers of a Layered Framework; The Basics for Business Layer; The Basics for Utility Layer; The Basics for Operational Management Layer; The Basics for Audit, Balance, and Control Layer; Audit; Balance; Control; The Basics for Functional Layer; Layered Framework for High-Level Data Science and Engineering; Windows; Linux; Summary; Chapter 4: Business Layer; Business Layer; The Functional Requirements; General Functional Requirements; Specific Functional Requirements; Data Mapping Matrix; Sun Models; Dimensions. 
505 8 |a SCD Type 1â#x80;#x94;Only UpdateSCD Type 2â#x80;#x94;Keeps Complete History; SCD Type 3â#x80;#x94;Transition Dimension; SCD Type 4â#x80;#x94;Fast-Growing Dimension.; Facts; Intra-Sun Model Consolidation Matrix; Sun Model One; Sun Model Two; Sun Model Three; The Nonfunctional Requirements; Accessibility Requirements; Audit and Control Requirements; Availability Requirements; Backup Requirements; Capacity, Current, and Forecast; Capacity; Concurrency; Throughput Capacity; Storage (Memory); Storage (Disk); Storage (GPU); Year-on-Year Growth Requirements; Configuration Management; Deployment; Documentation; Disaster Recovery. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed March 2, 2018). 
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650 0 |a Data structures (Computer science) 
650 0 |a Database management. 
650 7 |a COMPUTERS / Databases / Data Mining.  |2 bisacsh 
650 7 |a Data structures (Computer science)  |2 fast  |0 (OCoLC)fst00887978 
650 7 |a Database management.  |2 fast  |0 (OCoLC)fst00888037 
650 1 4 |a Computer Science. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Big Data/Analytics. 
650 2 4 |a Big Data. 
650 2 4 |a Data Storage Representation. 
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contents Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Science Technology Stack; Rapid Information Factory Ecosystem; Data Science Storage Tools; Schema-on-Write and Schema-on-Read; Schema-on-Write Ecosystems; Schema-on-Read Ecosystems; Data Lake; Data Vault; Hubs; Links; Satellites; Data Warehouse Bus Matrix; Data Science Processing Tools; Spark; Spark Core; Spark SQL; Spark Streaming; MLlib Machine Learning Library; GraphX; Mesos; Akka; Cassandra; Kafka; Kafka Core; Kafka Streams; Kafka Connect; Elastic Search; R; Scala., PythonMQTT (MQ Telemetry Transport); Whatâ#x80;#x99;s Next?; Chapter 2: Vermeulen-Krennwallner-Hillman-Clark; Windows; Linux; Itâ#x80;#x99;s Now Time to Meet Your Customer; Vermeulen PLC; Krennwallner AG; Hillman Ltd; Clark Ltd; Processing Ecosystem; Scala; Apache Spark; Apache Mesos; Akka; Apache Cassandra; Kafka; Message Queue Telemetry Transport; Example Ecosystem; Python; Ubuntu; CentOS/RHEL; Windows; Is Python3 Ready?; Python Libraries; Pandas; Ubuntu; Centos/RHEL; PIP; Matplotlib; Ubuntu; CentOS/RHEL; PIP; NumPy; SymPy; Scikit-Learn; R; Ubuntu; CentOS/RHEL; Windows; Development Environment; R Studio., UbuntuCentOS/RHEL; Windows; R Packages; Data.Table Package; ReadR Package; JSONLite Package; Ggplot2 Package; Amalgamation of R with Spark; Sample Data; IP Addresses Data Sets; Customer Data Sets; Logistics Data Sets; Post Codes; Warehouse Data Set; Shop Data Set; Exchange Rate Data Set; Profit-and-Loss Statement Data Set; Summary; Chapter 3: Layered Framework; Definition of Data Science Framework; Cross-Industry Standard Process for Data Mining (CRISP-DM); Business Understanding; Data Understanding; Data Preparation; Modeling; Evaluation; Deployment., Homogeneous Ontology for Recursive Uniform SchemaThe Top Layers of a Layered Framework; The Basics for Business Layer; The Basics for Utility Layer; The Basics for Operational Management Layer; The Basics for Audit, Balance, and Control Layer; Audit; Balance; Control; The Basics for Functional Layer; Layered Framework for High-Level Data Science and Engineering; Windows; Linux; Summary; Chapter 4: Business Layer; Business Layer; The Functional Requirements; General Functional Requirements; Specific Functional Requirements; Data Mapping Matrix; Sun Models; Dimensions., SCD Type 1â#x80;#x94;Only UpdateSCD Type 2â#x80;#x94;Keeps Complete History; SCD Type 3â#x80;#x94;Transition Dimension; SCD Type 4â#x80;#x94;Fast-Growing Dimension.; Facts; Intra-Sun Model Consolidation Matrix; Sun Model One; Sun Model Two; Sun Model Three; The Nonfunctional Requirements; Accessibility Requirements; Audit and Control Requirements; Availability Requirements; Backup Requirements; Capacity, Current, and Forecast; Capacity; Concurrency; Throughput Capacity; Storage (Memory); Storage (Disk); Storage (GPU); Year-on-Year Growth Requirements; Configuration Management; Deployment; Documentation; Disaster Recovery.
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spelling Vermeulen, Andreas François author., Practical data science a guide to building the technology stack for turning data lakes into business assets Andreas François Vermeulen, Berkeley, CA Apress 2018, ©2018, 1 online resource (xxv, 805 pages) : illustrations (some color)., text rdacontent, computer rdamedia, online resource rdacarrier, Includes index., Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Science Technology Stack; Rapid Information Factory Ecosystem; Data Science Storage Tools; Schema-on-Write and Schema-on-Read; Schema-on-Write Ecosystems; Schema-on-Read Ecosystems; Data Lake; Data Vault; Hubs; Links; Satellites; Data Warehouse Bus Matrix; Data Science Processing Tools; Spark; Spark Core; Spark SQL; Spark Streaming; MLlib Machine Learning Library; GraphX; Mesos; Akka; Cassandra; Kafka; Kafka Core; Kafka Streams; Kafka Connect; Elastic Search; R; Scala., PythonMQTT (MQ Telemetry Transport); Whatâ#x80;#x99;s Next?; Chapter 2: Vermeulen-Krennwallner-Hillman-Clark; Windows; Linux; Itâ#x80;#x99;s Now Time to Meet Your Customer; Vermeulen PLC; Krennwallner AG; Hillman Ltd; Clark Ltd; Processing Ecosystem; Scala; Apache Spark; Apache Mesos; Akka; Apache Cassandra; Kafka; Message Queue Telemetry Transport; Example Ecosystem; Python; Ubuntu; CentOS/RHEL; Windows; Is Python3 Ready?; Python Libraries; Pandas; Ubuntu; Centos/RHEL; PIP; Matplotlib; Ubuntu; CentOS/RHEL; PIP; NumPy; SymPy; Scikit-Learn; R; Ubuntu; CentOS/RHEL; Windows; Development Environment; R Studio., UbuntuCentOS/RHEL; Windows; R Packages; Data.Table Package; ReadR Package; JSONLite Package; Ggplot2 Package; Amalgamation of R with Spark; Sample Data; IP Addresses Data Sets; Customer Data Sets; Logistics Data Sets; Post Codes; Warehouse Data Set; Shop Data Set; Exchange Rate Data Set; Profit-and-Loss Statement Data Set; Summary; Chapter 3: Layered Framework; Definition of Data Science Framework; Cross-Industry Standard Process for Data Mining (CRISP-DM); Business Understanding; Data Understanding; Data Preparation; Modeling; Evaluation; Deployment., Homogeneous Ontology for Recursive Uniform SchemaThe Top Layers of a Layered Framework; The Basics for Business Layer; The Basics for Utility Layer; The Basics for Operational Management Layer; The Basics for Audit, Balance, and Control Layer; Audit; Balance; Control; The Basics for Functional Layer; Layered Framework for High-Level Data Science and Engineering; Windows; Linux; Summary; Chapter 4: Business Layer; Business Layer; The Functional Requirements; General Functional Requirements; Specific Functional Requirements; Data Mapping Matrix; Sun Models; Dimensions., SCD Type 1â#x80;#x94;Only UpdateSCD Type 2â#x80;#x94;Keeps Complete History; SCD Type 3â#x80;#x94;Transition Dimension; SCD Type 4â#x80;#x94;Fast-Growing Dimension.; Facts; Intra-Sun Model Consolidation Matrix; Sun Model One; Sun Model Two; Sun Model Three; The Nonfunctional Requirements; Accessibility Requirements; Audit and Control Requirements; Availability Requirements; Backup Requirements; Capacity, Current, and Forecast; Capacity; Concurrency; Throughput Capacity; Storage (Memory); Storage (Disk); Storage (GPU); Year-on-Year Growth Requirements; Configuration Management; Deployment; Documentation; Disaster Recovery., Online resource; title from PDF title page (SpringerLink, viewed March 2, 2018)., Computer science., Data structures (Computer science), Database management., COMPUTERS / Databases / Data Mining. bisacsh, Data structures (Computer science) fast (OCoLC)fst00887978, Database management. fast (OCoLC)fst00888037, Computer Science., Data Mining and Knowledge Discovery., Big Data/Analytics., Big Data., Data Storage Representation., Business & Economics Industries Computer Industry. bisacsh, Computers Database Management General. bisacsh, Computers System Administration Storage & Retrieval. bisacsh, Business mathematics & systems. bicssc, Databases. bicssc, Data mining. bicssc, Data mining., Big data., Computers Database Management Data Mining. bisacsh, Electronic books., Electronic book., ELD ebook, LDL ebooks ONIX to marcxml transformation using Record_Load-eBooks_Legal_Deposit_onix2marc_v2-1.xsl 20181213 com.springer.onix.9781484230541 Uk, (OCoLC)1026491074, British Library HMNTS DRT ELD.DS.371345
spellingShingle Vermeulen, Andreas François, Practical data science: a guide to building the technology stack for turning data lakes into business assets, Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Science Technology Stack; Rapid Information Factory Ecosystem; Data Science Storage Tools; Schema-on-Write and Schema-on-Read; Schema-on-Write Ecosystems; Schema-on-Read Ecosystems; Data Lake; Data Vault; Hubs; Links; Satellites; Data Warehouse Bus Matrix; Data Science Processing Tools; Spark; Spark Core; Spark SQL; Spark Streaming; MLlib Machine Learning Library; GraphX; Mesos; Akka; Cassandra; Kafka; Kafka Core; Kafka Streams; Kafka Connect; Elastic Search; R; Scala., PythonMQTT (MQ Telemetry Transport); Whatâ#x80;#x99;s Next?; Chapter 2: Vermeulen-Krennwallner-Hillman-Clark; Windows; Linux; Itâ#x80;#x99;s Now Time to Meet Your Customer; Vermeulen PLC; Krennwallner AG; Hillman Ltd; Clark Ltd; Processing Ecosystem; Scala; Apache Spark; Apache Mesos; Akka; Apache Cassandra; Kafka; Message Queue Telemetry Transport; Example Ecosystem; Python; Ubuntu; CentOS/RHEL; Windows; Is Python3 Ready?; Python Libraries; Pandas; Ubuntu; Centos/RHEL; PIP; Matplotlib; Ubuntu; CentOS/RHEL; PIP; NumPy; SymPy; Scikit-Learn; R; Ubuntu; CentOS/RHEL; Windows; Development Environment; R Studio., UbuntuCentOS/RHEL; Windows; R Packages; Data.Table Package; ReadR Package; JSONLite Package; Ggplot2 Package; Amalgamation of R with Spark; Sample Data; IP Addresses Data Sets; Customer Data Sets; Logistics Data Sets; Post Codes; Warehouse Data Set; Shop Data Set; Exchange Rate Data Set; Profit-and-Loss Statement Data Set; Summary; Chapter 3: Layered Framework; Definition of Data Science Framework; Cross-Industry Standard Process for Data Mining (CRISP-DM); Business Understanding; Data Understanding; Data Preparation; Modeling; Evaluation; Deployment., Homogeneous Ontology for Recursive Uniform SchemaThe Top Layers of a Layered Framework; The Basics for Business Layer; The Basics for Utility Layer; The Basics for Operational Management Layer; The Basics for Audit, Balance, and Control Layer; Audit; Balance; Control; The Basics for Functional Layer; Layered Framework for High-Level Data Science and Engineering; Windows; Linux; Summary; Chapter 4: Business Layer; Business Layer; The Functional Requirements; General Functional Requirements; Specific Functional Requirements; Data Mapping Matrix; Sun Models; Dimensions., SCD Type 1â#x80;#x94;Only UpdateSCD Type 2â#x80;#x94;Keeps Complete History; SCD Type 3â#x80;#x94;Transition Dimension; SCD Type 4â#x80;#x94;Fast-Growing Dimension.; Facts; Intra-Sun Model Consolidation Matrix; Sun Model One; Sun Model Two; Sun Model Three; The Nonfunctional Requirements; Accessibility Requirements; Audit and Control Requirements; Availability Requirements; Backup Requirements; Capacity, Current, and Forecast; Capacity; Concurrency; Throughput Capacity; Storage (Memory); Storage (Disk); Storage (GPU); Year-on-Year Growth Requirements; Configuration Management; Deployment; Documentation; Disaster Recovery., Computer science., Data structures (Computer science), Database management., COMPUTERS / Databases / Data Mining., Computer Science., Data Mining and Knowledge Discovery., Big Data/Analytics., Big Data., Data Storage Representation., Business & Economics Industries Computer Industry., Computers Database Management General., Computers System Administration Storage & Retrieval., Business mathematics & systems., Databases., Data mining., Big data., Computers Database Management Data Mining., Electronic books., Electronic book.
title Practical data science: a guide to building the technology stack for turning data lakes into business assets
title_auth Practical data science a guide to building the technology stack for turning data lakes into business assets
title_full Practical data science a guide to building the technology stack for turning data lakes into business assets Andreas François Vermeulen
title_fullStr Practical data science a guide to building the technology stack for turning data lakes into business assets Andreas François Vermeulen
title_full_unstemmed Practical data science a guide to building the technology stack for turning data lakes into business assets Andreas François Vermeulen
title_short Practical data science
title_sort practical data science a guide to building the technology stack for turning data lakes into business assets
title_sub a guide to building the technology stack for turning data lakes into business assets
topic Computer science., Data structures (Computer science), Database management., COMPUTERS / Databases / Data Mining., Computer Science., Data Mining and Knowledge Discovery., Big Data/Analytics., Big Data., Data Storage Representation., Business & Economics Industries Computer Industry., Computers Database Management General., Computers System Administration Storage & Retrieval., Business mathematics & systems., Databases., Data mining., Big data., Computers Database Management Data Mining., Electronic books., Electronic book.
topic_facet Computer science., Data structures (Computer science), Database management., COMPUTERS / Databases / Data Mining., Computer Science., Data Mining and Knowledge Discovery., Big Data/Analytics., Big Data., Data Storage Representation., Business & Economics, Computers, Business mathematics & systems., Databases., Data mining., Big data., Industries, Computer Industry., Database Management, General., System Administration, Storage & Retrieval., Data Mining., Electronic books., Electronic book.