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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."
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd.,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Morgan, Andrew. | |
| 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. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 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. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 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 Data mining. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 7 | |a COMPUTERS |x Databases |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Amend, Antoine. | |
| 700 | 1 | |a George, David. | |
| 700 | 1 | |a Hallett, Matthew. | |
| 776 | 0 | 8 | |i Print version: |a Morgan, Andrew. |t Mastering Spark for Data Science. |d Birmingham : Packt Publishing, ©2017 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1495812 |z Texto completo |
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