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41 lines
1.8 KiB
Markdown
41 lines
1.8 KiB
Markdown
##### data vs. information
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- **Data** is just raw facts (like the number 42).
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- But 42 could mean: age, shoe size, stock amount, etc.
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- **Information** is when you give meaning to the data.
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- Example: “Age = 42” gives context and becomes useful.
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##### Big Data implementations
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- **Delta** – *Sentiment analysis* (e.g., of customer feedback).
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- **Netflix** – *User Behavioral Analysis* (e.g., what you watch and when).
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- **Time Warner** – *Customer segmentation* (dividing customers into groups).
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- **Volkswagen** – *Predictive support* (e.g., predict car issues).
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- **Visa** – *Fraud detection*.
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- **China government** – *Security Intelligence* (National security).
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- **Weather forecasting** – *Weather prediction models* to predicting the weather.
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- **Hospitals** – Diagnosing diseases using *machine learning* on images.
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- **Amazon** – *Price optimization*.
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- **Facebook** – Targeted advertising using *user profiling*.
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##### Design Principles for Big Data
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1. **Horizontal Growth** – Add more machines instead of stronger ones.
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2. **Distributed Processing** – Split work across machines.
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3. **Process where Data is** – Don’t move data, move the code.
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4. **Simplicity of Code** – Keep logic understandable.
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5. **Recover from Failures** – Systems should self-heal.
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6. **Idempotency** – Running the same job twice shouldn’t break results.
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##### Big Data SLA (Service Level Agreement)
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define performance expectations
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- **Reliability** – Will the data be there?
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- **Consistency** – Is the data accurate across systems?
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- **Availability** – Is the system always accessible?
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- **Freshness** – How up-to-date is the data?
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- **Response time** – How fast do queries return?
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* Other concerns:
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- **Cost**
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- **Scalability**
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- **Performance**
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> Next [[Cloud Services]] |