Rapid recognition of highly complex patterns.
Graph Data Analytics
Relational databases reach their performance limits in complex applications with large, unstructured data volumes. The result is that response times increase rapidly and real-time data processing is not feasible in this way. Graph data or graph databases, on the other hand, do not suffer any loss of performance with extremely large numbers of interlinked data records. They are particularly suitable for large amounts of strongly interlinked (real-time) data and for unstructured data sets that do not fit into any relational structure or into tables. This is because the data relationships do not have to be calculated in a complex way, but are easily comprehensible due to the existing structures. Countless directed relationships between the individual pieces of information can thus be represented.
In social or logistical networks as well as in computer networks and telecommunication systems, information is available as a network anyway and can be implemented directly as a graph. The mapping of interrelationships and dependencies can thus be done quickly. This enables you to develop new services or improve existing ones on this basis.
Benefits Graph Data
Unlimited flexibility and expandability
Flexible data modelling
More accurate representation of reality
Analytical visualisation of the complex interrelationships
Your analysis solution with graph data
We support you
We support you in the technical and methodological conception, the selection and application of suitable modelling approaches as well as in the systematic evaluation of the insights gained.
With our services, we present a way for you to benefit from graph data technology. We provide you with technical and methodological concepts for:
- Graph databases
- Graph-based data models
- Graph visualisations;Graph analysis
- Migration of source data into the graph databases
Inspiration for your application
Success Stories
Graph Data Analytics
Graph analysis and dashboards on conflicts in Congo