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  1. Big Data Visualization
    learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization
    Autor*in: Miller, James D.
    Erschienen: February 2017; © 2017
    Verlag:  Packt, Birmingham

    Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Big Data Visualization -- An explanation of data visualization --... mehr

    Standort:
    Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Südwestdeutscher Bibliotheksverbund (SWB)

    Standort:
    Hochschulbibliothek Friedensau
    Signatur:
    Online-Ressource
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Gemeinsamer Bibliotheksverbund (GBV)

     

    Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Big Data Visualization -- An explanation of data visualization -- Conventional data visualization concepts -- Training options -- Challenges of big data visualization -- Big data -- Using Excel to gauge your data -- Pushing big data higher -- The 3Vs -- Volume -- Velocity -- Variety -- Categorization -- Such are the 3Vs -- Data quality -- Dealing with outliers -- Meaningful displays -- Adding a fourth V -- Visualization philosophies -- More on variety -- Velocity -- Volume -- All is not lost -- Approaches to big data visualization -- Access, speed, and storage -- Entering Hadoop -- Context -- Quality -- Displaying results -- Not a new concept -- Instant gratifications -- Data-driven documents -- Dashboards -- Outliers -- Investigation and adjudication -- Operational intelligence -- Summary -- Chapter 2: Access, Speed, and Storage with Hadoop -- About Hadoop -- What else but Hadoop? -- IBM too! -- Log files and Excel -- An R scripting example -- Points to consider -- Hadoop and big data -- Entering Hadoop -- AWS for Hadoop projects -- Example 1 -- Defining the environment -- Getting started -- Uploading the data -- Manipulating the data -- A specific example -- Conclusion -- Example 2 -- [Sorting] -- Sorting -- Parsing the IP -- Summary -- Chapter 3: Understanding Your Data Using R -- [Definitions and explanations] -- Definitions and explanations -- Comparisons -- Contrasts -- Tendencies -- Dispersion -- Adding context -- About R -- R and big data -- Example 1 -- Digging in with R -- Example 2 -- Definitions and explanations -- No looping -- Comparisons -- Contrasts -- Tendencies -- Dispersion -- Summary -- Chapter 4: Addressing Big Data Quality -- Data quality categorized DataManager -- DataManager and big data -- Some examples -- Some reformatting -- A little setup -- Selecting nodes -- Connecting the nodes -- The work node -- Adding the script code -- Executing the scene -- Other data quality exercises -- What else is missing? -- Status and relevance -- Naming your nodes -- More examples -- Consistency -- Reliability -- Appropriateness -- Accessibility -- Other Output nodes -- Summary -- Chapter 5: Displaying Results Using D3 -- About D3 -- D3 and big data -- Some basic examples -- Getting started with D3 -- A little down time -- Visual transitions -- Multiple donuts -- More examples -- Another twist on bar chart visualizations -- One more example -- Adopting the sample -- Summary -- Chapter 6: Dashboards for Big Data - Tableau -- About Tableau -- Tableau and big data -- Example 1 - Sales transactions -- Adding more context -- Wrangling the data -- Moving on -- A Tableau dashboard -- Saving the workbook -- Presenting our work -- More tools -- Example 2 -- What's the goal? - purpose and audience -- Sales and spend -- Sales v Spend and Spend as % of Sales Trend -- Tables and indicators -- All together now -- Summary -- Chapter 7: Dealing with Outliers Using Python -- About Python -- Python and big data -- Outliers -- Options for outliers -- Delete -- Transform -- Outliers identified -- Some basic examples -- Testing slot machines for profitability -- Into the outliers -- Handling excessive values -- Establishing the value -- Big data note -- Setting outliers -- Removing Specific Records -- Redundancy and risk -- Another point -- If Type -- Reused -- Changing specific values -- Setting the Age -- Another note -- Dropping fields entirely -- More to drop -- More examples -- A themed population -- A focused philosophy -- Summary -- Chapter 8: Big Data Operational Intelligence with Splunk -- About Splunk Splunk and big data -- Splunk visualization -  real-time log analysis -- IBM Cognos -- Pointing Splunk -- Setting rows and columns -- Finishing with errors -- Splunk and processing errors -- Splunk visualization - deeper into the logs -- New fields -- Editing the dashboard -- More about dashboards -- Summary -- Index

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Leibniz-Institut für Deutsche Sprache, Bibliothek
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781785284168
    RVK Klassifikation: ST 265
    Schlagworte: Big data; Information visualization
    Umfang: 1 Online-Ressource (299 pages)
  2. Big data visualization
    learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization
    Autor*in: Miller, James D.
    Erschienen: 2017
    Verlag:  Packt Publishing, Birmingham, UK

    Standort:
    Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Südwestdeutscher Bibliotheksverbund (SWB)
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Leibniz-Institut für Deutsche Sprache, Bibliothek
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781785284168; 1785284169; 9781785281945
    Schlagworte: Big data; Information visualization; Big data; Information visualization
    Umfang: 1 Online-Ressource (1 volume)