TECHNOLOGIES

Most of our projects involve large databases, complex data analysis, high performance computing and secure network operations. We use and promote following open source and free technologies in our projects. Our ideology is to provide free or low cost large scale data analytics solutions without restricting licensing schemes

Python. Wonderful language to perform data analysis and build ad-hoc programs for quick prototyping. Supported with several data analysis modules.

R. Statistical analysis and programming language with lots of potential. Invaluable tool for any data scientist. Includes a full suite of statistical analysis tools and functions which you can expand with your own functions. It is possible to write your own functions in C for really fast calculations.

Java. We have certain reservations against Java (due to security issues), but it is so widely used that it would be impractical to ban it altogether.

C and C++. Modern C and C++ is very useful when you have to develop very fast application. This is especially important in data analysis with really large data set.

Javascript. Probably one of the most important programming languages right now, and very versatile. Although you may envision it only as a “helper” language for HTML,  it is more than that. JQuery, JSON and node.js are important tools for web based applications.

CakePHP. Very usable PHP based MVC framework. If you are already familiar with PHP, then this is a quick way to build SAAS projects. Interesting point is that you can integrate your R or Python analysis programs with PHP.

Groovy, Grails and Griffon. Groovy is a language that is interpreted in JVM. However, it is much faster to develop, but integrates with all Java features. Grails and Griffon are a MVC/MVT frameworks based on Groovy.

Django. Python based application MVC/MVT framework.

Unity. It may sound suprising – since Unity is a game development environment – but we have found out its usefulness in visualising dynamic simulations and producing 3D presentations of complex data.

Eclipse. Free application development environment which you can expand with dozens of add-ons and tools. Whether you develop Java, Python, Groovy or PHP applications, you will find it invaluable. Or if you happen to develop models of business processes there is even BPMN2 Modeller.

RStudio. Invaluable development environment for every R user.

PostgreSQL and MariaDB. Free relational DB engines. We favour PostgreSQL, although MariaDB is more widely used. MariaDB is a community-developed fork of the MySQL RDMS.

Hadoop. Foundation for any Big Data project where you have to store hundreds of terabytes data in various forms. Technically you use consumer level hardware in large quantities to build Hadoop clusters. Large scale data warehousing, in a manner.

HBase.

Pig.

MongoDB. Document oriented Big Data database.

Mahout. Machine learning module for Big Data. It is still missing many relevant machine learning algorithms, but evolves rapidly. And since it is an open source project, you can help and implement whatever algorithms you may need.