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Data Engineering

Data engineers are on the front line of data technology. They are the first to tackle the challenges of the manifold dimensions of data: scale, quality, type, privacy, frequency and velocity.

To facilitate a smooth and efficient data innovation process from collecting and integrating data through data modeling to advanced analytics, we develop the data infrastructure with market leading technologies and methodologies.

Smart Data Platform components

Our Data Engineering practice is responsible for establishing the data storage and data integration layers of a unified data platform that enables responding to complex questions, populating reports and discovering dependencies between originally independent data.

We design and implement the full process of ELT/ETL and data update including the necessary data model according to your business and analytics requirements. We have built several applications and blueprints to facilitate infrastructure implementation, data integration, algorithmic data processing, and configuration both in the cloud and in an on-premise environment.

Both relational and non-relational data, as well as IoT data from Point of Care sensors and file-based local registers from any kind of data source, are integrated into a central datalake allowing the storage of all of your data assets in a modern centrally managed data platform.

 
Smart Data Platform components
Our Data Engineering practice is responsible for establishing the data storage and data integration layers of a unified data platform that enables responding to complex questions, populating reports and discovering dependencies between originally independent data.

We design and implement the full process of ELT/ETL and data update including the necessary data model according to your business and analytics requirements. We have built several applications and blueprints to facilitate infrastructure implementation, data integration, algorithmic data processing, and configuration both in the cloud and in an on-premise environment.
Both relational and non-relational data, as well as IoT data from Point of Care sensors and file-based local registers from any kind of data source, are integrated into a central datalake allowing the storage of all of your data assets in a modern centrally managed data platform.
Future-proof data analytics and reporting in all sectors based on
  • siloed or legacy data sources only integrated on the business process level
  • inconsistent, high inertia data in a different structure, quality, frequency, content etc.
  • operative system data with hidden complex dependencies on an organizational, legal, technological and infrastructural level
  • discovering hidden complex dependencies (organizational, legal, technological and infrastructural) from legacy operative system data
  • global company-owned sensitive business information that cannot be collected or copied
  • medical records and other highly sensitive data
  • revenue management analytics
  • consumer analytics

Application areas

Application Areas

Case Studies

News & Blog InnoHealth DataLake

GDPR-proof clinical research system

The aim of the InnoHealth Datalake project is to develop and implement the concept of a novel complex IT system capable of collecting, storing, and analyzing all types of health data generated in healthcare activities and services at the University of Pécs, together with all relevant external data. It also serves as a prototype for domestic and regional healthcare systems.

The Datalake’s principal capabilities were identified according to current and future needs of healthcare including data collection (independent of size, type, and source of data), data storage, and data analyses by state-of-the-art analytical methods and tools supporting healthcare services and R&D&I activities.