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Data Visualization in Big Data Analytics
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- 2. THE GROWING YEARS Y2 Ensuredata to increase in depths from bytes till petabytes of data. Y1 Introduce data Y3 Focus information and other data is just discarded
- 3. Meaning ► Data Visualizationis the graphical representation of data in the form of charts, diagrams, pictures etc. ► Data visualization refers to those set of techniques used to communicate insights from data through visual representation. ► The main goal is to distill large datasets into visual graphics to allow for easy understanding of complex relationships within the data. Data Insights Visualize
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- 5. Soap Bubble ClusterVisualization
- 6. Significance ► In theage of technological development and growing big data it is necessary to make data more understandable and provide data with better visualization techniques. ► the ability to absorb information quickly, improve insights and make faster decisions; ► an increased understanding of the next steps that must be taken to improve the organization; ► an improved ability to maintain the audience's interest with information they can understand.
- 7. Ways of DataVisualization Data Visualization Charts Tables Graphs Maps Info graphics Dashboards
- 8. Traits of Meaningfuldata 1. It is visually appealing 2. It is scalable 3. It must provide audience with right information 4. It must be accessible 5. It enables rapid development and deployment 6. High volume ❖ More data means more chances of getting something interesting. 7. Historical ❖ Helps in understanding present and future by predicting patterns from the past. ❖ Helps in getting better insights of data.
- 9. 8. Multivariate ❖ Datashould be multi-valued so that every aspect of data to be considered. 9. Detailed ❖ It should be the complete understanding of detailed analysis of data. 10. Clean ❖ Keeps data consistent despite changes over time. ❖ Keeps data segregated in different forms. 11. Clear ❖ Data should be familiar in similar terms for better understanding.
- 10. Data traits shouldbe:- ► Meaningful – People use it on a regular basis and can make relevant decisions by comprehensive view. ► Desirable – It is not only easy to use but also pleasant to use. ► Usable – People can use them achieve their goals easily and quickly.
- 11. Brief History :Data growth from 1990s and onwards… ► Since , the past few years the size of the data is growing at enormous rate that even cannot be predicted. ► The data grows its insights from petabytes to exabytes of data. ► In 1990s only 3% of the data grows as time grows decade shifts the amount of data growth also increases from 3 % to 31 % and till 85 %. ► The rate of growth is so rapid that human efforts even cannot recognize insights from growing data. ► So, the data is sometimes discarded from the memory even without analyzing it from memory.
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- 13. ► To havebetter understandability of data we need proper tools and techniques for processing the data and getting better outcomes out of it. ► Some tools used for analyzing big data are:- 1. Apache Cassandra 2. Hadoop 3. Plotly 4. Mongo DB 5. Drill 6. Storm 7. Splunk
- 14. Features Apache Cassandra Hadoop PlotlyMongo DB Drill Storm Splunk Mode of software Free open source distributed. Open source and free source Open source Open source and free software Open source and distributed framework Open source and distributed. Proprietary tool and real time platform. Data Processing SQL queries, streaming data ,machine learning Batch Processing System Interactive graphs and user defined images No SQL database, Document oriented database Distributed execution environment for large scale data Supports Stream Processing Machine data processing Language Support Supports java,python, Node.js Supports java, c ,C++,Ruby,per l,python Supports java script, python, R plotly Supports python, javascript, c, c++, php,Ruby,Perl . Supports ANSI SQL(industry standard query language) Supports non JVM Languages. supports python,jav a,java script. Data Flow Data is written into a commit log as well as in memory store. Map reduce computation data flow does not have any loops. It is a chain of stages. Track the flow of individual items through Plotly Sankey. Data streams focussed on massive flow of data from multiple fire horses and then routing to systems. It makes it to use SQL query non relational databases and file systems. Designeas a directed acyclic graph with streams used to process the data. Pipeline executes a series of processors which operates on the data.
- 15. ensuring data is secured. active directory kerbores andLDAP for authenticatio n. tion between browser and server by encrypting data in transmission with SSL. makes it easy to control access with role based user manageme nt. petabytes. n capabilities. a paid license. It quickly detects and responds to internal and external attacks . Latency Lower latency by replicating across multiple data centers. It offers higher latency. It offers reduced latency due to caching. It offers low latency analytics as it cant analyse semi or unstructured data. It offers Low latency. Exteremely low latency. It offers moderatel y high latency as it works with real time architectu re. Fault tolerance It is tolerant of both network partitions and nodes dying. Map reduce is highly fault tolerant. No need to start the application It has trusted advisor fault tolerant review Replica set deployment architecture supports fault tolerance. JDBC connection improves fault tolerance If any node dies storm will automatical ly restart for fault It offers apprecia bly high fault tolerance .
- 16. Power of VisualPerception ► Visual Perception is the ability to understand the things of surrounding environment via the ability of what the eyes see. ► Visual perception is the process of absorbing what one sees, organizing it in the brain, and making sense of it. ► With poor visual perception, we would have significant deficits in many cognitive processes or cognitive activities that affect our mental content. ► These deficits could significantly impact learning.
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- 18. Stages of Perception ORGANIZATION SELECTIONINTERPRETATION
- 19. Making abstract datavisible ► We are collecting data, but we lag in what we can do with it. ► When we have too much information , it becomes nearly impossible to understand the insights of information depicted so we use some ways of interpreting the raw data in the form of visualization. ► So, that it becomes easy to understand the raw data and fetch information out of it.
- 20. Area Chart Bubble Cloud BarChart Bullet Graph Circle View Dot Distribution Map
- 21. Information Visualization ► Thevisualization process is a transformation of data in one representation to another, mostly to a representation better observable by humans. ► The following steps of a visualization process can be found in any problem area: 1.Data preparation 2.Encoding 3.Presentation 4.Interaction.
- 22. Visualization Process 1.Identification- Firststep of the visualization process, is used to identify relevant entities and events that the visualization will deal with. 2. Encoding- The second step encoding deals with problems how the data will be displayed. The questions to be considered are oriented to efficiency, aesthetic, understanding, similarity etc. These aspects of visualization play major role for humans that will be involved in the visualization process, because when the visualization is not understandable or uses nonstandard visuals, the benefits of visualization may get lost. 3. Presentation & Interaction- The final phase is presentation and interaction and should answer questions about how the visualization objects are displayed and which interaction possibilities are offered to the user. For each specific visualization the vocabulary used is also important.
- 23. ► Visual explorationtechniques can be classified according to three orthogonal criteria:- 1. Data to be visualized 2. The visualization technique 3. Interaction 4. Distortion technique used.
- 24. Building blocks ofinformation visualization ► Precision ► Size ► Color ► Shape
- 25. Analytical Techniques ► Somedifferent kinds of techniques for analyzes are used:- 1. SWOT 2. MOST
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- 27. SWOT Analyzes ► S= Strength ► W = Weakness ► O = Opportunities ► T = Threats
- 28. • Advantages • Unique& low cost resources STRENGTH • Disadvantages or limitations • Improvement areas Weakness
- 29. •Good chances •Chances of improving OPPORTUNITIES •Externalrisks •Obstacles •Competitors THREATS
- 30. MOST Analyzes ► M= Mission ► O = Objective ► S = Strategies ► T = Tactics
- 31. •Option to achieve •Strategies materialize d byaction. •Goals that help in achieving •Purpose Mission Objectives Strategies Tactics
- 32. Effective 3D look Modifiable Easilyimplemented Animation Symmetric Advantages of Visual Perception