Anand Kumar

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Burton G. Malkiel
“Invariably, the hottest stocks or funds in one period are the worst performers in the next.”
Burton G. Malkiel, A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing

Martin Kleppmann
“However, guarantees such as reading your own writes are useful, and I don’t think that it is productive to tell everyone “eventual consistency is inevitable — suck it up and learn to deal with it” (at least not without good guidance on how to deal with it).”
Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Martin Kleppmann
“Surveillance As a thought experiment, try replacing the word data with surveillance, and observe if common phrases still sound so good [93]. How about this: “In our surveillance-driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.”
Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

“We can adapt to this stress with adequate aerobic function, and when we allow sufficient time for recovery, which, for most people means a maximum of one or two hard training efforts a week.”
Philip Maffetone, The MAF Method: A Personalized Approach to Health and Fitness

Martin Kleppmann
“We are already seeing car insurance premiums linked to tracking devices in cars, and health insurance coverage that depends on people wearing a fitness tracking device. When surveillance is used to determine things that hold sway over important aspects of life, such as insurance coverage or employment, it starts to appear less benign. Moreover, data analysis can reveal surprisingly intrusive things: for example, the movement sensor in a smartwatch or fitness tracker can be used to work out what you are typing (for example, passwords) with fairly good accuracy [98]. And algorithms for analysis are only going to get better.”
Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

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