Mastering Spark for data science /:
"Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products."
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing Ltd.,
2017.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products." |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource |
| ISBN: | 1785888285 9781785888281 |
Internformat
MARC
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| 245 | 1 | 0 | |a Mastering Spark for data science / |c Andrew Morgan, Antoine Amend, Matthew Hallett, David George ; foreword by Harry Powell. |
| 260 | |a Birmingham, UK : |b Packt Publishing Ltd., |c 2017. | ||
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| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a Includes index. | ||
| 520 | |a "Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products." | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Copyright; Credits; Foreword; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: The Big Data Science Ecosystem; Introducing the Big Data ecosystem; Data management; Data management responsibilities; The right tool for the job; Overall architecture; Data Ingestion; Data Lake; Reliable storage; Scalable data processing capability; Data science platform; Data Access; Data technologies; The role of Apache Spark; Companion tools; Apache HDFS; Advantages; Disadvantages; Installation; Amazon S3; Advantages; Disadvantages. | |
| 505 | 8 | |a InstallationApache Kafka; Advantages; Disadvantages; Installation; Apache Parquet; Advantages; Disadvantages; Installation; Apache Avro; Advantages; Disadvantages; Installation; Apache NiFi; Advantages; Disadvantages; Installation; Apache YARN; Advantages; Disadvantages; Installation; Apache Lucene; Advantages; Disadvantages; Installation; Kibana; Advantages; Disadvantages; Installation; Elasticsearch; Advantages; Disadvantages; Installation; Accumulo; Advantages; Disadvantages; Installation; Summary; Chapter 2: Data Acquisition; Data pipelines; Universal ingestion framework. | |
| 505 | 8 | |a Introducing the GDELT news streamDiscovering GDELT in real-time; Our first GDELT feed; Improving with publish and subscribe; Content registry; Choices and more choices; Going with the flow; Metadata model; Kibana dashboard; Quality assurance; [Example 1 -- Basic quality checking, no contending users]; Example 1 -- Basic quality checking, no contending users; Example 2 -- Advanced quality checking, no contending users; Example 3 -- Basic quality checking, 50% utility due to contending users; Summary; Chapter 3: Input Formats and Schema; A structured life is a good life; GDELT dimensional modeling. | |
| 505 | 8 | |a GDELT modelFirst look at the data; Core global knowledge graph model; Hidden complexity; Denormalized models; Challenges with flattened data; Issue 1 -- Loss of contextual information; Issue 2: Re-establishing dimensions; Issue 3: Including reference data; Loading your data; Schema agility; Reality check; GKG ELT; Position matters; Avro; Spark-Avro method; Pedagogical method; When to perform Avro transformation; Parquet; Summary; Chapter 4: Exploratory Data Analysis; The problem, principles and planning; Understanding the EDA problem; Design principles; General plan of exploration; Preparation. | |
| 505 | 8 | |a Introducing mask based data profilingIntroducing character class masks; Building a mask based profiler; Setting up Apache Zeppelin; Constructing a reusable notebook; Exploring GDELT; GDELT GKG datasets; The files; Special collections; Reference data; Exploring the GKG v2.1; The Translingual files; A configurable GCAM time series EDA; Plot.ly charting on Apache Zeppelin; Exploring translation sourced GCAM sentiment with plot.ly; Concluding remarks; A configurable GCAM Spatio-Temporal EDA; Introducing GeoGCAM; Does our spatial pivot work?; Summary; Chapter 5: Spark for Geographic Analysis. | |
| 630 | 0 | 0 | |a Spark (Electronic resource : Apache Software Foundation) |0 http://id.loc.gov/authorities/names/no2015027445 |
| 630 | 0 | 7 | |a Spark (Electronic resource : Apache Software Foundation) |2 fast |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Apprentissage automatique. | |
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| 700 | 1 | |a Amend, Antoine. | |
| 700 | 1 | |a George, David. | |
| 700 | 1 | |a Hallett, Matthew. | |
| 758 | |i has work: |a Mastering Spark for data science (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGBKJPMByyyXcFYPVy4FDq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Morgan, Andrew. |t Mastering Spark for Data Science. |d Birmingham : Packt Publishing, ©2017 |
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Datensatz im Suchindex
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| contents | Cover; Copyright; Credits; Foreword; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: The Big Data Science Ecosystem; Introducing the Big Data ecosystem; Data management; Data management responsibilities; The right tool for the job; Overall architecture; Data Ingestion; Data Lake; Reliable storage; Scalable data processing capability; Data science platform; Data Access; Data technologies; The role of Apache Spark; Companion tools; Apache HDFS; Advantages; Disadvantages; Installation; Amazon S3; Advantages; Disadvantages. InstallationApache Kafka; Advantages; Disadvantages; Installation; Apache Parquet; Advantages; Disadvantages; Installation; Apache Avro; Advantages; Disadvantages; Installation; Apache NiFi; Advantages; Disadvantages; Installation; Apache YARN; Advantages; Disadvantages; Installation; Apache Lucene; Advantages; Disadvantages; Installation; Kibana; Advantages; Disadvantages; Installation; Elasticsearch; Advantages; Disadvantages; Installation; Accumulo; Advantages; Disadvantages; Installation; Summary; Chapter 2: Data Acquisition; Data pipelines; Universal ingestion framework. Introducing the GDELT news streamDiscovering GDELT in real-time; Our first GDELT feed; Improving with publish and subscribe; Content registry; Choices and more choices; Going with the flow; Metadata model; Kibana dashboard; Quality assurance; [Example 1 -- Basic quality checking, no contending users]; Example 1 -- Basic quality checking, no contending users; Example 2 -- Advanced quality checking, no contending users; Example 3 -- Basic quality checking, 50% utility due to contending users; Summary; Chapter 3: Input Formats and Schema; A structured life is a good life; GDELT dimensional modeling. GDELT modelFirst look at the data; Core global knowledge graph model; Hidden complexity; Denormalized models; Challenges with flattened data; Issue 1 -- Loss of contextual information; Issue 2: Re-establishing dimensions; Issue 3: Including reference data; Loading your data; Schema agility; Reality check; GKG ELT; Position matters; Avro; Spark-Avro method; Pedagogical method; When to perform Avro transformation; Parquet; Summary; Chapter 4: Exploratory Data Analysis; The problem, principles and planning; Understanding the EDA problem; Design principles; General plan of exploration; Preparation. Introducing mask based data profilingIntroducing character class masks; Building a mask based profiler; Setting up Apache Zeppelin; Constructing a reusable notebook; Exploring GDELT; GDELT GKG datasets; The files; Special collections; Reference data; Exploring the GKG v2.1; The Translingual files; A configurable GCAM time series EDA; Plot.ly charting on Apache Zeppelin; Exploring translation sourced GCAM sentiment with plot.ly; Concluding remarks; A configurable GCAM Spatio-Temporal EDA; Introducing GeoGCAM; Does our spatial pivot work?; Summary; Chapter 5: Spark for Geographic Analysis. |
| ctrlnum | (OCoLC)981985497 |
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| discipline | Informatik |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-10-25T16:23:43Z |
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| publisher | Packt Publishing Ltd., |
| record_format | marc |
| spelling | Morgan, Andrew. Mastering Spark for data science / Andrew Morgan, Antoine Amend, Matthew Hallett, David George ; foreword by Harry Powell. Birmingham, UK : Packt Publishing Ltd., 2017. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes index. "Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products." Print version record. Cover; Copyright; Credits; Foreword; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: The Big Data Science Ecosystem; Introducing the Big Data ecosystem; Data management; Data management responsibilities; The right tool for the job; Overall architecture; Data Ingestion; Data Lake; Reliable storage; Scalable data processing capability; Data science platform; Data Access; Data technologies; The role of Apache Spark; Companion tools; Apache HDFS; Advantages; Disadvantages; Installation; Amazon S3; Advantages; Disadvantages. InstallationApache Kafka; Advantages; Disadvantages; Installation; Apache Parquet; Advantages; Disadvantages; Installation; Apache Avro; Advantages; Disadvantages; Installation; Apache NiFi; Advantages; Disadvantages; Installation; Apache YARN; Advantages; Disadvantages; Installation; Apache Lucene; Advantages; Disadvantages; Installation; Kibana; Advantages; Disadvantages; Installation; Elasticsearch; Advantages; Disadvantages; Installation; Accumulo; Advantages; Disadvantages; Installation; Summary; Chapter 2: Data Acquisition; Data pipelines; Universal ingestion framework. Introducing the GDELT news streamDiscovering GDELT in real-time; Our first GDELT feed; Improving with publish and subscribe; Content registry; Choices and more choices; Going with the flow; Metadata model; Kibana dashboard; Quality assurance; [Example 1 -- Basic quality checking, no contending users]; Example 1 -- Basic quality checking, no contending users; Example 2 -- Advanced quality checking, no contending users; Example 3 -- Basic quality checking, 50% utility due to contending users; Summary; Chapter 3: Input Formats and Schema; A structured life is a good life; GDELT dimensional modeling. GDELT modelFirst look at the data; Core global knowledge graph model; Hidden complexity; Denormalized models; Challenges with flattened data; Issue 1 -- Loss of contextual information; Issue 2: Re-establishing dimensions; Issue 3: Including reference data; Loading your data; Schema agility; Reality check; GKG ELT; Position matters; Avro; Spark-Avro method; Pedagogical method; When to perform Avro transformation; Parquet; Summary; Chapter 4: Exploratory Data Analysis; The problem, principles and planning; Understanding the EDA problem; Design principles; General plan of exploration; Preparation. Introducing mask based data profilingIntroducing character class masks; Building a mask based profiler; Setting up Apache Zeppelin; Constructing a reusable notebook; Exploring GDELT; GDELT GKG datasets; The files; Special collections; Reference data; Exploring the GKG v2.1; The Translingual files; A configurable GCAM time series EDA; Plot.ly charting on Apache Zeppelin; Exploring translation sourced GCAM sentiment with plot.ly; Concluding remarks; A configurable GCAM Spatio-Temporal EDA; Introducing GeoGCAM; Does our spatial pivot work?; Summary; Chapter 5: Spark for Geographic Analysis. Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445 Spark (Electronic resource : Apache Software Foundation) fast Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Exploration de données (Informatique) Apprentissage automatique. Données volumineuses. COMPUTERS Databases Data Mining. bisacsh Big data fast Data mining fast Machine learning fast Amend, Antoine. George, David. Hallett, Matthew. has work: Mastering Spark for data science (Text) https://id.oclc.org/worldcat/entity/E39PCGBKJPMByyyXcFYPVy4FDq https://id.oclc.org/worldcat/ontology/hasWork Print version: Morgan, Andrew. Mastering Spark for Data Science. Birmingham : Packt Publishing, ©2017 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1495812 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1495812 Volltext |
| spellingShingle | Morgan, Andrew Mastering Spark for data science / Cover; Copyright; Credits; Foreword; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: The Big Data Science Ecosystem; Introducing the Big Data ecosystem; Data management; Data management responsibilities; The right tool for the job; Overall architecture; Data Ingestion; Data Lake; Reliable storage; Scalable data processing capability; Data science platform; Data Access; Data technologies; The role of Apache Spark; Companion tools; Apache HDFS; Advantages; Disadvantages; Installation; Amazon S3; Advantages; Disadvantages. InstallationApache Kafka; Advantages; Disadvantages; Installation; Apache Parquet; Advantages; Disadvantages; Installation; Apache Avro; Advantages; Disadvantages; Installation; Apache NiFi; Advantages; Disadvantages; Installation; Apache YARN; Advantages; Disadvantages; Installation; Apache Lucene; Advantages; Disadvantages; Installation; Kibana; Advantages; Disadvantages; Installation; Elasticsearch; Advantages; Disadvantages; Installation; Accumulo; Advantages; Disadvantages; Installation; Summary; Chapter 2: Data Acquisition; Data pipelines; Universal ingestion framework. Introducing the GDELT news streamDiscovering GDELT in real-time; Our first GDELT feed; Improving with publish and subscribe; Content registry; Choices and more choices; Going with the flow; Metadata model; Kibana dashboard; Quality assurance; [Example 1 -- Basic quality checking, no contending users]; Example 1 -- Basic quality checking, no contending users; Example 2 -- Advanced quality checking, no contending users; Example 3 -- Basic quality checking, 50% utility due to contending users; Summary; Chapter 3: Input Formats and Schema; A structured life is a good life; GDELT dimensional modeling. GDELT modelFirst look at the data; Core global knowledge graph model; Hidden complexity; Denormalized models; Challenges with flattened data; Issue 1 -- Loss of contextual information; Issue 2: Re-establishing dimensions; Issue 3: Including reference data; Loading your data; Schema agility; Reality check; GKG ELT; Position matters; Avro; Spark-Avro method; Pedagogical method; When to perform Avro transformation; Parquet; Summary; Chapter 4: Exploratory Data Analysis; The problem, principles and planning; Understanding the EDA problem; Design principles; General plan of exploration; Preparation. Introducing mask based data profilingIntroducing character class masks; Building a mask based profiler; Setting up Apache Zeppelin; Constructing a reusable notebook; Exploring GDELT; GDELT GKG datasets; The files; Special collections; Reference data; Exploring the GKG v2.1; The Translingual files; A configurable GCAM time series EDA; Plot.ly charting on Apache Zeppelin; Exploring translation sourced GCAM sentiment with plot.ly; Concluding remarks; A configurable GCAM Spatio-Temporal EDA; Introducing GeoGCAM; Does our spatial pivot work?; Summary; Chapter 5: Spark for Geographic Analysis. Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445 Spark (Electronic resource : Apache Software Foundation) fast Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Exploration de données (Informatique) Apprentissage automatique. Données volumineuses. COMPUTERS Databases Data Mining. bisacsh Big data fast Data mining fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/names/no2015027445 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2012003227 |
| title | Mastering Spark for data science / |
| title_auth | Mastering Spark for data science / |
| title_exact_search | Mastering Spark for data science / |
| title_full | Mastering Spark for data science / Andrew Morgan, Antoine Amend, Matthew Hallett, David George ; foreword by Harry Powell. |
| title_fullStr | Mastering Spark for data science / Andrew Morgan, Antoine Amend, Matthew Hallett, David George ; foreword by Harry Powell. |
| title_full_unstemmed | Mastering Spark for data science / Andrew Morgan, Antoine Amend, Matthew Hallett, David George ; foreword by Harry Powell. |
| title_short | Mastering Spark for data science / |
| title_sort | mastering spark for data science |
| topic | Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445 Spark (Electronic resource : Apache Software Foundation) fast Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Exploration de données (Informatique) Apprentissage automatique. Données volumineuses. COMPUTERS Databases Data Mining. bisacsh Big data fast Data mining fast Machine learning fast |
| topic_facet | Spark (Electronic resource : Apache Software Foundation) Data mining. Machine learning. Big data. Exploration de données (Informatique) Apprentissage automatique. Données volumineuses. COMPUTERS Databases Data Mining. Big data Data mining Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1495812 |
| work_keys_str_mv | AT morganandrew masteringsparkfordatascience AT amendantoine masteringsparkfordatascience AT georgedavid masteringsparkfordatascience AT hallettmatthew masteringsparkfordatascience |