Big data visualization
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for bi...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham B3 2PB, UK. :
Packt Publishing
2017.
Birmingham, England ; Mumbai, [India] : 2017. |
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630329706719 |
Tabla de Contenidos:
- 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.
