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NoSQL Case Study: Riak for the Danish Healthcase System
InfoQ just published Kresten Krab Thorup presentation at GOTO conference, Riak on Drugs (and the Other Way Around), covering details about the Danish healthcare system built on top of Riak for high availability, scalability and to run off multiple data centers. Now we have both sides of the case study of building a nationwide healthcase system using Riak and Gigaspaces XAP. Original title and link: NoSQL Case Study: Riak for the Danish Healthcase System (NoSQL database©myNoSQL)
myNoSQL: A NoSQL Use Case: URL Shorteners
Last week, I have mentioned a post by Gleicon Moraes on ☞ building an URL shortener using MongoDB and alternatively Redis. There is also a ☞ GitHub project by Sean Cribbs implementing the same scenario using Riak and Ruby-based Sinatra. Update: Jan Lehnardt was quick to point me…
Being Honest
Wow. It’s been like, wow!
It’s been a long time, I shouldn’t have left you. 1: It’s real, 2: It’s being stolen, 3: We’re here to save an art.
52 Blocks
Wow, it’s like I remember being in north park dropping gems to my friends as I went back, to a style called Hands. But then came the day, my Host asked me about a style called 52. I looked, yes, but they were doing it wrong. That wasn’t 52.
No it’s not Real.
No, it’s not real. But it was like seeing Kali Daga n Mano for the first time. Why do you touch me. Wait, If there doing it wrong… It lead me to many months, searching. So many people were showing this style, it was hands, but not hands. It was more Jail House, but it had a hip hop flow.
So I reached out
Then one night I hit some information, a video where someone explained the history of this art. It was the middle of the night, but I tried the number.
A month later I went to train.
Kinda how I’m feeling today.
(via espirituarete)
5,6,7,8 … Fierce
I just find this amazing.
(Source: everythingyoulovetohate)
And there were bloody bits on my laptop keyboard, the linoleum, the bookshelf…
Alejo Henao, Untitled, 2010
Data Science & The Role of the Data Scientist
From the Wikibon blog infographic about data science and the data scientist:
Data science can be broken down into four essential parts:
- mining data: collecting and formatting the information
- statistics: information analysis
- interpret: representation or visualization
- leverage: implications of the data, application of the data, interaction using the data and predictions formed from studying it
The skills of a data scientist:
- Hacking and Computer Science: knowing how to take advantage of computers and the internet to create data-mining formulas
- Expertise in Mathematics, Statistics, Data Mining: Pulling important statistics and coherently organizing them using mathematic prowess and computer formulas
- Creativity and Insight: Knowing what statistics are important and how to leverage them
In a recent post under the title Data beats math, Jeff Jonas[1] wrote:
Over the years, folks have often asked me what kind of math am I using to create large scale, real-time, context accumulating systems (e.g., NORA). Some fond of Bayesian speculate I am using Bayesian techniques. Some ask if I am using neural networks or heuristics. A math professor said I was doing advanced work in the field of Set Theory.
My answer is always, “I don’t know any math. I didn’t finish high school. But I can explain how it works, step-by-step, and it is really quite simple.”
So data science starts with the passionate interest for the data. Then you are adding tools, processes, algorithms, and science to discover the secrets hidden inside data.
(via anxioussparrow)
