Real Time and Big Data
The world is moving fast. Immediacy of information and analytics knowledge is a need increasingly requested by people, companies, etc. At the same time many sources of information are offering data in Real Time Streaming over the network. Using efficiently Streaming technologies of Big Data ecosystem allows us to deal with immediacy problems.
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Social Network and Big Data
Social networks like Twitter represents a continuous source of data. The huge amount of information coming out from there all the time is very useful in analytics. Looking at such ocean of information, has been detected even health threats massively expanded around the world. Using the right technologies for getting information on that source will help many industries improve businesses.
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Big Data Analytics
Business Intelligence technologies allow us to make an interactive analysis (OLAP), responding to different types of queries in constant time. However with the born of Big Data, traditional technologies used for this purposes are not efficient enough to deal with big amounts of data. For solving the problem of making analytics over billions of rows in some cases, technologies like, Kylin (MOLAP), Lens (ROLAP) are emerging.
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Semantic Web and Big Data
There are more than 1 billion web pages in the world, and the number is growing. Processing and analyzing such amount of information is a clear example of Big Data. Using Big Data technologies for semantic processing, we can get extract useful information from different types of web sites (news, blogs, search engines) making Real Time analytics and integrating it with our analytical applications.
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Graph Analysis
The connectivity of the elements in the "Information Era" is a fact, that means that all is connected o related in some way. This induct us to use Graph as a best Data Structure for dealing with data where relation between elements is determinant in the analysis. Using DB and Graphs's Algorithm we can make very interesting analysis that offer a different perspective of the data.
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Machine Learning
In many occasions the most relevant information that can be extracted from data can't be obtained making an SQL query or a programmed Algorithm, generally because they require some type of reasoning, that can't be achieved using traditional Queries or Algorithms. In this cases using Machine Learning, techniques that helps us to race up the analytics to a highest level. Some examples could be Predictive Analysis, Recommendation Systems, Pattern Recognition. In out demo we have exposed some.
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Internet of Things
Using the connectivity of any electronic devices we use every day, is revolutionizing the way we obtain and process information. The possibility of sensors, to send data about its measures or state to a remote server, makes us be aware at Real Time, about the behavior of an specific process and monitorize it or analyze it in Real Time. There are many interesting use cases about this kind of solution, in this section we show some of them.
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