This slides includes the basic introduction of Big Data,

1. Big Data
&
Big DataTechnologies
Presenters
Swikar Bhandari
Yaju Shrestha
Yubraj Ghimire 7/12/20171

2. Overview
 Introduction to Big Data
 Characteristics of Big Data
 Challenges in Big Data
 Big DataTrends
 Data Scientist & their roles
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3. Introduction to Big Data
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 Information that can’t be processed or analyzed using
traditional processes or tools.
 Data sets that are so large and complex
 Data which are difficult to capture, store, process,
search and analysis
 ‘Big-data’ is similar to‘Small-data’, but bigger.

4. Characteristics of Big Data
Volume
• Data size
• Data is generated by machines, networks and human
interaction on systems like social media
• 2.5 Exabytes of data produced everyday which is equivalent
to 90 years of HD video
Velocity
• The pace of flow of data from sources like business,
machines, network and human interaction with social medias
or mobiles
• The data flow is massive and continuous
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5. Characteristics of Big Data
Variety
• Data heterogeneity: Structured and Unstructured data
• Many sources and types of data both structured and
unstructured.
• Data comes in the form of emails, photos, videos,
monitoring devices, PDFs, audio, etc
Veracity
• Uncertainty of accuracy and authenticity of data
• Biases, noise and abnormality in data.
• Data that is being stored, and mined meaningful to the
problem being analyzed or not.
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6. Characteristics of Big Data
Validity
• The issue of validity meaning is the data correct and accurate
for the intended use.
• Valid data is key to making the right decisions
Volatility
• How long is data valid and how long should it be stored.
• Data need to determine at what point is data no longer
relevant to the current analysis.
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7. Challenges in Big Data
 Fault tolerance: ability to handle failures
 Scalability : ability to handle data with time
 Heterogeneity: ability to handle various kinds of data
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8. Big Data in Information System
 Unstructured data handling capability
 Real time data processing
 Predictive analytics and in-memory analytics
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9. Big Data Trends
 NoSQL database: for handling unstructured data
 Cloud based analytics: migrating data to cloud platform
 Deep learning: algorithms are used for mining data
 In memory analytics: speed up analytical processing.
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10. Data Scientist
 Person that analyses and interprets data to assist in
decision making.
 The people who understand how to fish out answers to
important business questions from today's tsunami of
unstructured information
 A hybrid of data hacker, analyst, communicator, and
trusted adviser
.
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11. Roles and Skills Of Data Scientist
 Use technologies that make taming big data possible,
including Hadoop, and related open-source tools, cloud
computing, and data visualization.
 Make discoveries while swimming in pool of data
 Bring structure to large quantities of formless data and make
analysis possible
 Write code
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12. Roles and Skills Of Data Scientist
 Communicate what they’ve learned and suggest its
implications for new business directions
 Fashion their own tools and even conduct academic-style
research
 Be creative in displaying information visually and making the
patterns they find clear and compelling
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13. THANK YOU
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