We define Big Data architecture by creating design for customer's specific needs.
We will design data processing system, its examination and implement the analysis in the field of machine learning. Subsequently, we will organise a training focused on the utilisation of these instruments.
Big Data analysis is based on the examination of data provided by the customer, which will be supplemented by other sources. We will find correlations and relations between phenomenon and merits.
Data processing, storage and analysis require adequate infrastructure. The key characteristic is distributed architecture and linear scalability. Batch-oriented systems such as Hadoop or NoSQL databases have their own specifications and they differ from the real-time data processing, where Apache Kafka or Apache Flink are the most wide-spread. We are proficient at both.
Machine learning represents a field of artificial intelligence studying methods of programming, whose goal is to acquire the ability to learn. By means of statistics, it tries to understand circumstances, which generate data – often in order to test various hypotheses. It tries to find patterns in this data that are understandable to people.
This is used in practice, for example, in customer segmentation in marketing campaigns and the production of individualised offers.
Predictive analysis is a tool of highly sophisticated analytical methods aimed at finding correlations among various types of data (often seemingly unrelated). It is an evolving field of data analysis with growing number of algorithms. Essentially, it is oriented on the search for characteristics and models of behaviour. Many algorithms reveal correlations among data, based on which it is possible to determine the cause.
It is widely used in sales, when based on the customer's browsing habits, it is possible to predict a certain type of behaviour, preferences and risk rate.