##### data vs. information
- **Data** is just raw facts (like the number 42).
    - But 42 could mean: age, shoe size, stock amount, etc.

- **Information** is when you give meaning to the data.
    - Example: “Age = 42” gives context and becomes useful.

##### Big Data implementations
- **Delta** – *Sentiment analysis* (e.g., of customer feedback).
- **Netflix** – *User Behavioral Analysis* (e.g., what you watch and when).
- **Time Warner** – *Customer segmentation* (dividing customers into groups).
- **Volkswagen** – *Predictive support* (e.g., predict car issues).
- **Visa** – *Fraud detection*.
- **China government** – *Security Intelligence* (National security).
- **Weather forecasting** – *Weather prediction models* to predicting the weather.
- **Hospitals** – Diagnosing diseases using *machine learning* on images.
- **Amazon** – *Price optimization*.
- **Facebook** – Targeted advertising using *user profiling*.
##### Design Principles for Big Data
1. **Horizontal Growth** – Add more machines instead of stronger ones.
2. **Distributed Processing** – Split work across machines.
3. **Process where Data is** – Don’t move data, move the code.
4. **Simplicity of Code** – Keep logic understandable.
5. **Recover from Failures** – Systems should self-heal.
6. **Idempotency** – Running the same job twice shouldn’t break results.

##### Big Data SLA (Service Level Agreement)
define performance expectations

- **Reliability** – Will the data be there?
- **Consistency** – Is the data accurate across systems?
- **Availability** – Is the system always accessible?
- **Freshness** – How up-to-date is the data?
- **Response time** – How fast do queries return?

* Other concerns:
	- **Cost**
	- **Scalability**
	- **Performance**

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