| Description |
1 online resource |
|
text file |
| Note |
Includes index. |
| Summary |
"Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products." |
| 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. |
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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. |
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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. |
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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. |
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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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Spark (Electronic resource : Apache Software Foundation)
|
|
Spark (Electronic resource : Apache Software Foundation) |
|
Data mining.
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Data mining. |
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Machine learning.
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Machine learning. |
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Big data.
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Big data. |
| Genre/Form |
Electronic books.
|
| Added Author |
Amend, Antoine.
|
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George, David.
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Hallett, Matthew.
|
| Other Form: |
Print version: Morgan, Andrew. Mastering Spark for Data Science. Birmingham : Packt Publishing, ©2017 |
| ISBN |
1785888285 (electronic book) |
|
9781785888281 (electronic book) |
|
1785882147 |
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