Data engineering
Companies today produce vast amounts of data – from information about customers, value of sales, and stock prices to an assortment of internal data. Understanding what this data tells us is sometimes neither easy nor simple, so companies use data engineering to gain valuable insights from everything that happens with their business.
The company needs to design and build systems intended to receive and process immense amounts of diverse data generated at high speed by various sources and integrate it into decision support systems.
The goal is to find as many practical applications of information as possible in order to achieve faster business success.
During the implementation, we use a set of technologies that enable fast and simple horizontal scalability of all system components. This solves the bottleneck problem and addresses a wide range of challenges, starting with the sheer amount of data and ending with the need for almost instantaneous availability.
Collection, adaptation, and unified approach in the presentation of data enables development of new business models based on maximum exploitation of available information.
We primarily use tools and technologies from Cloudera data management technology package during implementation of data engineering projects. Technologies for information storage, cataloging, and advanced data exploitation are also available in the same solution ecosystem (these are, most often, advanced analytics (AI) and machine learning (ML) projects).
Use case example
The organization wants to take a proactive approach in maintenance of a complex energy system, reduce incidence of failures and, consequently, reduce system downtimes. By collecting and analyzing data that flows sequentially and often simultaneously from a large number of sensors, the system establishes correlations that reveal causes of previous downtimes. It is designed to withstand high peak loads (1000 readings per millisecond).
It can process collected data at an exceptional speed: a maximum of half an hour passes from reading the data to presenting the results. Modular architecture ensures simple and fast scalability of the entire system, making it easy to increase data processing power and source capacity.
