The Big Data argument speaks quite clearly for itself: the better a brand knows its customers, its interaction with those customers becomes more meaningful, ultimately increasing the likelihood that the customer takes some sort of action whether it be a purchase, a positive review, a recommendation or otherwise, and repeat that action in the future. However, the tech landscape provides a backdrop that basically changes with the seasons, so as the hardware changes, so does the consumer.
Data scientists are a rare breed. Part mathematician, part business professional and part computer programmer, these curious individuals play and discover in the world of big data, spotting trends and leading companies around the world toward lower costs, higher returns and better overall products.
What happens when you plot billions of geotagged Tweets on a map? You can see the arteries of the world.
A stunning mind map resuming R resources to work on big data. Very interesting portrait of the state of the art with R!
This seminal article highlights the dangers of reckless applications and scaling of data science techniques that have worked well for small, medium-size and large data. We illustrate the problem with flaws in big data trading, and propose solutions.
There has been released a video of Twitter‘s Kevin Weil speaking at Strange Loop earlier this year on how the company uses NoSQL. Weil is quick to point out that Twitter is heavily dependent on MySQL. However, Twitter does employ NoSQL solutions for many purposes for which MySQL isn’t ideal. According to Weil, Twitter users generate 12 terrabytes…