Remove 2004 Remove Open Source Remove System Design
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Interviewing is a noisy prediction problem

Erik Bernhardsson

So we can choose to spend one hour on system design, one hour on algorithms, etc. System design. I like system design questions (“how would you build this feature”). Same goes with open source contributions, blog posts, or other things. Algorithms. Github portfolio. Actual job performance.

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Interviewing is a noisy prediction problem

Erik Bernhardsson

So we can choose to spend one hour on system design, one hour on algorithms, etc. System design. I like system design questions (“how would you build this feature”). Same goes with open source contributions, blog posts, or other things. Algorithms. Github portfolio. Actual job performance.

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25 Feb Cloudera Federal Forum in Tysons Corner: Amazing agenda filled with lessons learned and best practices

CTOvision

Dr. Daniel Duffy is head of the NASA Center for Climate Simulation (NCCS, Code 606.2), which provides high performance computing, storage, networking, and data systems designed to meet the specialized needs of the Earth science modeling communities. High Performance Computing Lead, NASA Center for Climate Simulation (NCCS).

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Join Architects, Planners, Program Managers, Data Scientists at 4th Annual Cloudera Federal Forum in DC 25 Feb

CTOvision

Dr. Daniel Duffy is head of the NASA Center for Climate Simulation (NCCS, Code 606.2), which provides high performance computing, storage, networking, and data systems designed to meet the specialized needs of the Earth science modeling communities. High Performance Computing Lead, NASA Center for Climate Simulation (NCCS).

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Friction

LeanEssays

Amazon built its cloud with the same philosophy – they knew that at the scale they intended to pursue, everything would fail sooner rather than later, so automated failure detection and recovery had to be designed into the system. Want to analyze a lot of data? You will undoubtedly consider Hadoop, originally developed at Yahoo!

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Grown-Up Lean

LeanEssays

I then make a sustained argument from the Linux experience for the proposition that “Given enough eyeballs, all bugs are shallow”, suggest productive analogies with other self-correcting systems of selfish agents, and conclude with some exploration of the implications of this insight for the future of software.