Big Data Visualization
Bring scalability and dynamics to your Big Data visualization
1. Auflage
Erschienen am 13. Januar 2025
304 Seiten
978-1-78528-416-8 (ISBN)
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Inhalt
- 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
