Big data is one of the most used buzzword nowadays, even more we could probably say like Dan Ariely without any fear of mistake that “big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
Although this is just a joke, thinking in depth we could easily see some questions that arise here even for someone that hasn’t had contact contact with software development : Big ? But how big is Big ? When do we need to think in “big data terms” ? Can we implement a simple solution now and think later about a “big data” one ? What are the costs ? Which is the benefit ?. All of these are legitimate questions. How can you as a company realize that you have to go to “big data” processing level ? And if we go to the technical level and suppose that you have already understood that you need this type of solution, then you will get other problems. Which is the best technology to use for your use case ? The differences are small, but the impact can vary hugely. Think only a about how many noSQL databases exist and choosing one that doesn’t fit for you use case can leverage a lot of extra work to adapt and in the end the result will not be the best.
Big Data Research Group focuses on 3 main directions.
We want to understand and explore the edges of current technologies using real life use cases with a focus on distributed computing, machine learning and business intelligence.
In this context, we investigate the technical problems that exists in the big data industry, learn from them and provide the community with the best solutions.
More specific: investigate, propose hypothesis, write code, test,document, deploy.
We are ready (and hopefully others will be too) to do voluntary work, and provide free proof of concepts, that match specific use cases related to big data and machine learning.
In addition to that, everyone (person or company), can ask/request for our expertise. However, everything we do will be given for free to the community: as code on github, or simple documentation through blog-posts.
3. Build a community of highly skilled experts.
We decided to overcome all the questions, but not in the classic way of going to a consulting company, which implies high costs and only some limited knowledge from the members ( that of course can be extensive but not all-knowing ), but with a community / group that can have a win-win relation with the industry.
We know that building a community and doing voluntary work is time consuming. Moreover we know that it can be tough and frustrating to try to find solution for specific use case and not to build general purpose software. However, we are ready to tackle any challenging problem, because we know that “everything is theoretically impossible, until it is done.” – Robert A. Heinlein
So, hello world ! Nice to meet you !
Big Data Research Group Team