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|x 021000
|2 bisacsh
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|a UAMI
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| 100 |
1 |
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|a Sremack, Joe,
|e author.
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| 245 |
1 |
0 |
|a Big data forensics--learning Hadoop investigations :
|b perform forensic investigations on Hadoop clusters with cutting-edge tools and techniques /
|c Joe Sremack.
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| 246 |
3 |
0 |
|a Perform forensic investigations on Hadoop clusters with cutting-edge tools and techniques
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| 264 |
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1 |
|a Birmingham, UK :
|b Packt Publishing,
|c 2015.
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| 300 |
|
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|a 1 online resource :
|b illustrations
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| 336 |
|
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|a text
|b txt
|2 rdacontent
|
| 337 |
|
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Community experience distilled
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| 588 |
0 |
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|a Online resource; title from cover (Safari, viewed September 20, 2015).
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| 500 |
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|a Includes index.
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| 505 |
0 |
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|a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Starting Out with Forensic Investigations and Big Data; Computer forensics overview; The forensic process; Identification; Collection; Analysis; Presentation; Other investigation considerations; Equipment; Evidence management; Investigator training and certification; The post-investigation process; What is Big Data?; The four Vs of Big Data; Big Data architecture and concepts; Big Data forensics; Metadata preservation; Collection methods; Collection verification; Summary.
|
| 505 |
8 |
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|a Chapter 2: Understanding Hadoop Internals and ArchitectureThe Hadoop architecture; The components of Hadoop; The Hadoop Distributed File System; The Hadoop configuration files; Hadoop daemons; Hadoop data analysis tools; Hive; HBase; Pig; Managing files in Hadoop; File permissions; Trash; Log files; File compression and splitting; Hadoop SequenceFile; The Hadoop archive files; Data serialization; Packaged jobs and JAR files; The Hadoop forensic evidence ecosystem; Running Hadoop; LightHadoop; Amazon Web Services; Loading Hadoop data; Importing sample data for testing; Summary.
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| 505 |
8 |
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|a Chapter 3: Identifying Big Data EvidenceIdentifying evidence; Locating sources of data; Compiling data requirements; Reviewing the system architecture; Interviewing staff and reviewing the documentation; Assessing data viability; Identify data sources in noncooperative situations; Data collection requirements; Data source identification; Structured and unstructured data; Data collection types; In-house or third-party collection; An investigator-led collection; The chain of custody documentation; Summary; Chapter 4: Collecting Hadoop File System Data; Forensically collecting a cluster system.
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| 505 |
8 |
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|a Physical versus remote collectionsHDFS collections through the host operating system; Imaging the host operating system; Imaging a mounted HDFS partition; Targeted collection from a Hadoop client; The Hadoop shell command collection; Collecting HDFS files; HDFS targeted data collection; Hadoop Offline Image and Edits Viewers; Collection via Sqoop; Other HDFS collection approaches; Summary; Chapter 5: Collecting Hadoop Application Data; Application collection approaches; Backups; Query extractions; Script extractions; Software extractions; Validating application collections.
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| 505 |
8 |
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|a Collecting Hive evidenceLoading Hive data; Identifying Hive evidence; Hive backup collection; Hive query collection; Hive query control totals; Hive metadata and log collection; The Hive script collection; Collecting HBase evidence; Loading HBase data; Identifying HBase evidence; The HBase backup collection; The HBase query collection; HBase collection via scripts; HBase control totals; HBase metadata and log collection; Collecting other Hadoop application data and non-Hadoop data; Summary; Chapter 6: Performing Hadoop File System Analysis; The forensic analysis process.
|
| 546 |
|
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|a English.
|
| 590 |
|
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
|
| 630 |
0 |
0 |
|a Apache Hadoop.
|
| 630 |
0 |
7 |
|a Apache Hadoop
|2 fast
|
| 650 |
|
0 |
|a Big data.
|
| 650 |
|
0 |
|a Forensic sciences.
|
| 650 |
|
0 |
|a Data mining.
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| 650 |
|
6 |
|a Données volumineuses.
|
| 650 |
|
6 |
|a Criminalistique.
|
| 650 |
|
6 |
|a Exploration de données (Informatique)
|
| 650 |
|
7 |
|a forensic science.
|2 aat
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| 650 |
|
7 |
|a COMPUTERS
|x Database Management
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a COMPUTERS
|x Desktop Applications
|x Databases.
|2 bisacsh
|
| 650 |
|
7 |
|a COMPUTERS
|x System Administration
|x Storage & Retrieval.
|2 bisacsh
|
| 650 |
|
7 |
|a Big data
|2 fast
|
| 650 |
|
7 |
|a Data mining
|2 fast
|
| 650 |
|
7 |
|a Forensic sciences
|2 fast
|
| 776 |
0 |
8 |
|i Print version:
|a Sremack, Joe.
|t Big Data Forensics - Learning Hadoop Investigations.
|d Olton Birmingham : Packt Publishing Ltd, ©2015
|z 9781785288104
|
| 830 |
|
0 |
|a Community experience distilled.
|
| 856 |
4 |
0 |
|u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1056275
|z Texto completo
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| 938 |
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|a Askews and Holts Library Services
|b ASKH
|n AH29156399
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| 938 |
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|a EBL - Ebook Library
|b EBLB
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|b EBSC
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|a ProQuest MyiLibrary Digital eBook Collection
|b IDEB
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|b YANK
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