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Social Media World 2013 - Περήφανος Κώστας: Τhere in no such thing as Big Data
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2. There is no such thing as „Big Data‟
The hitchhiker‟s guide to the data universe
Kostas Perifanos, Senior Search and Analytics Engineer
Data Analytics & Visualization Team
19.06.2013
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The „Big Data‟ buzzword
Big Data is the top IT buzzword:
• 4Vs: Volume, Velocity, Variety, Veracity
• [Predictive] Analytics
• Social Media Data
• Networking / Graph
• Buz Buz Buz…
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The „Big Data‟ buzzword
A not so formal definition
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But what‟s this all about?
Is this the whole part of the picture?
• Everybody is trying to do “Big Data”
• Before “Big”, don‟t forget “Data”
“Most companies believe they have „Big Data Problems‟,
What they actually do have is „Big, Data Problems‟”
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Dealing with (potentially massive) datasets
The Paradigm Shift – Engineering
Traditional RDBMS – SQL world is perfectly fine for the majority of the
problems we are trying to solve. On top of that, we now have noSQL
technologies to deal with structured, unstructured or semistructured
data
• Escaping common pitfalls:
– Before “Big”, don‟t forget “Data”
– Avoid sub-optimal premature decisions
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Dealing with (potentially massive) datasets
The Paradigm Shift – Engineering plus Data science
Choose your tools wisely ..
• Try to avoid over-engineering-but do keep scaling in mind
• Define and store all necessary information
• Let the data speak for itself
• Choose your tools wisely
• Data will guide your engineers to choose the appropriate tools
• Most of the data problems can be solved in a (decent) laptop
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Dealing with (potentially massive) datasets
Asking the right questions
• Know your own business
• Are the questions supported by the existing data?
• What will be the impact in case additional data are required?
• What will be the expected/desired profit for your organization?
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Dealing with massive datasets
Building “Big Data” teams
• Your asset is your Team – Engineers/Data Scientists/Visualization
experts.
• Use open source where applicable – contribute back to the
community
• Use Lean/Agile Methodologies
– Maintain a strong Product Vision
• Attend conferences / trainings / trends
• Invest in Learning. Always.
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‘It is a capital mistake
to theorize before one
has data.’
Sir Arthur Conan Doyle