We met with CEO and Co – Founder of Radoop. Radoop is a big data analytics company with the goal of lowering the barrier for using Hadoop systems. The Radoop package allows even non-technical people to access, analyze and visualize big data, but also provides scripting and advanced predictive analytics for experts. It is integrated into RapidMiner, the most popular data mining tool, and pushes computations to an Apache Hadoop cluster.

So who are you and what are you doing ?

I’m Zoltan Prekopcsac.  I’m the co- founder and CEO of Radoop.  I personally have a background in data mining and machine learning .At Radoopwe are dealing with big data and allowing our customers to analyze large data sets in an easy to use interface. The Company is almost 3 years old now and the product has been launched a bit for more than a year ago.  We are slowly maturing and the product is very stable now.  We just need to go out their to other markets  now and spread the word about what we can do.

How did you come up with this idea?

I was working in academia and we had been working with very large data sets at the time.  Started working with Hadoop and realized how complex it is.  So it was my own pain that lead to this.  I couldn’t find any tools to work with.  So we had lots of experience with the graphical user interfaces, like RapidMiner, so it was an obvious choice to connect these 2 worlds, the distributive power of Hadoop based analytics and the ease of use interfaces like RapidMiner.

Could you tell me a little about your business ?

So many companies are looking into big data these days and one of the most popular platforms is Hadoop.  Hadoop is a very complex technology; you need usually, developers and programmers to work with that.  What we do is create a graphical user interface to simplify the usage of Hadoop.  You do not need weeks and months of training to do big data analytics.  You can simply use our interface and in a few hours you are able to generate repots and even create machine learning processes.

What males you unique ? 

Yeah, we are very specially focused on machine learning and analytics. What also is special, we are integrated with RapidMiner.  RapiMiner is one of the top advanced analytics tools. Recent Gartner survey has identified RapidMiner as a leader and we have a very strong integration.  So you get a very familiar interface if you are used to RapidMiner and you can combine in Hadoop analytics with RapidMiner so it is a very powerful combination.

What is your business model?

We license our product per user per year.  So customers can buy these licenses depending on the Hadoop cluster size and the support they need. We also sell some services, we help companies get started with big data, with the infrastructure and we also do analytics.

Are you looking to expand into other markets?

Yes of course, even if we’re based in Hungary we do have Schneider Electronics as a big client.  We also have other several medium sized companies from the US and from India as our clients.  We are doing our best to expand  by taking help from our partners who are based in the US, Germany and   Singapore.

Are you looking for any funding, any special talents to hire?

Yeah, we are always looking for talents

What kind of talents?

Usually we need programmers to develop platform, but also what is very challenging in the big data space is to find good business talent.  So sales guides, it’s a very complex IT solution, so they need to be well versed in IT but still have the business and sales skills.  That’s certainly a challenge. We can hire the programmers and developers from Hungary, because Hungary has a good education of computer science.

Radoop is a big data analytics company with the goal of lowering the barrier for using Hadoop systems. The Radoop package allows even non-technical people to access, analyze and visualize big data, but also provides scripting and advanced predictive analytics for experts. It is integrated into RapidMiner, the most popular data mining tool, and pushes computations to an Apache Hadoop cluster.

Melanie Mueller, Data Scientist from Harvard University, gives us an overview of her visit to Big Data TechCon conference in Boston. Melanie attended the conference from March 31 to April 2.

Big Data practitioners converged on Boston to attend Big Data TechCon , a Big Data training conference organized by BZ media. Topics covered both tools to deal with Big Data, such as current database and parallelization solutions, as well as analytics to derive insight from it.

For tools, hands-on tutorials were particular popular and often overcrowded, revealing the most coveted techniques. Classes to analyze social media streams were filled to the last chair, and a one-day crash course on Hadoop was booked out weeks before the conference.

In his tool-focused keynote, Sunil Venkayala from HP Vertica talked about Distributed R, an open source software that is still under development. Distributed R marries R and Hadoop, and promises to allow analysis of data that is too large for vanilla R. To this end, the Vertica team rewrites R routines to provide scalable high-performance on multiple nodes for distributed processing, while allowing users to use familiar GUIs and packages from R.

 Todd Cioffi from RapidMiner raised the question: Why every time we get a new data problem we start coding? He emphasized that we shouldn’t confuse the tools with the process, and that we need to focus on the questions rather than the tools in order to gain valuable insights. To ask the right questions, we need to move on from traditional business intelligence with its query and dashboard based reporting to modern advanced analytics with descriptive and predictive modeling. For example, a business intelligence question such as ‘Which packages are on which truck?’ should be replaced by an advanced analytics questions like ‘How can I optimally assign packages to trucks to minimize delivery time and usage of trucks?’

Scott Sokoloff from TEL and Will Ford from Alpine Data Labs illustrated common pitfalls when trying to leverage Big Data for business decisions. They emphasized that it is not enough to just follow the pattern sin the data and find actionable insights. The insights must also align with the company interests, and they must be actionable in the company’s particular environment. For example, proposing a strategy that will bring down product prices sounds good – but it might not go well with the sales team if their bonuses are based on the total dollar amount sold. Scott Sokoloff phrased his take home message as: Big data analytics is not about who is the smartest, it’s a relationship business.

Apart from the presentations, Big Data TechCon allowed for ample networking opportunities between the attendees– indeed, Big Data is a relationship business!

Melanie is a postdoc at Harvard University, where she is creating and munching data from biological experiments. Most of the time she she is trying to figure out what yeast cells have done while growing on a Petri dish. It turns out that automated data analysis with Matlab and Python can help a lot in this process! Before moving to Harvard University, Melanie obtained a PhD in Physics from the Max Planck Institute of Colloids and Interfaces in Potsdam, Germany, where she used mathematical modeling, computer simulations and analysis of experimental data to understand how molecular motors transport cargoes in cells.

Ingo Mierswa CEO of Rapidminer has stated that “Whatsapp was a good deal for facebook”. Ingo points out that with over 450 million users  WhatsApp is already a very strong player on the market. Going forward Ingo believes that the company can  reach an  user base of over 1 billion users as its user base is extremely high outside the US.

Taking Germany as an example Ingo notes that, “almost half of the German population,  are using WhatsApp on a regular basis”. Text analysis of Whatsapp messages can give facebook a deep insight into the “desires and interests of an important economy like Germany”.   Also, “usage of text analytics could further improve FB’s ability to offer personalized ads to their base”.   This additional data from Whatsapp could lead to building of “better predictive models and better placed ads”, which in turn could lead  to a 10 %  increase of revenue for FB. A 10% increase in revenue for FB could pay off the acquisition handsomely.