The data fabric principle ensures a centralized and uniform architecture that enables the integration of various services and technologies, both cloud and on-premise systems. The focus here is not only on technologies but also on a design concept to make various applications more efficient, including in the area of ML.
The data fabric principle ensures a centralized and uniform architecture that enables the integration of various services and technologies, both cloud and on-premise systems. The focus here is not only on technologies but also on a design concept to make various applications more efficient, including in the area of ML.
The data fabric principle ensures a centralized and uniform architecture that enables the integration of various services and technologies, both cloud and on-premise systems. The focus here is not only on technologies but also on a design concept to make various applications more efficient, including in the area of ML.
Data fabric is a design concept that represents a unified and centralized data architecture. It has an integration layer (fabric) and processes that serve to integrate and process the individual connections. It constantly performs analyses on existing metadata to support the implementation, provision and use of data across all environments, both on-premises and cloud.
The aim is to access data through user- and machine-supported analyses in order to obtain business value from the various relations of the existing data points, thereby supporting management in decision-making and providing efficient access to relevant data.
Initialization – basic requirements & framework conditions, scoping / project planning, working environment, project plan (time and migration plan), kick-off meeting.
Analysis and evaluation – careful elicitation and management of requirements to achieve the objectives, definition of the migration method and decision on the technologies and tools used. Definition of functional and non-functional requirements.
Functional and technical design – platform architecture, data modeling, standards, templates and best practices, interface definition, architecture data integration, final architecture and concepts.
Implementation – development of the elaborated architecture in iterations based on the previously collected requirements.
Testing, go-live, monitoring and operation – accompaniment of acceptance tests, error corrections, performance / SLA tests, test & acceptance protocols, deployment to the productive system, handover of operations, monitoring by monitoring tools, hypercare.
synvert accelerators – data strategy & architecture workshop, conceptual design template, synvert cloud integration patterns, naming convention templates.
Our data fabric service offers you a precise and structured approach based on best practices and extensive project experience. With our structured approach, we ensure that your business goals are achieved in clear, actionable steps.
Detailed knowledge of data architectures and their optimization.
Customizable solutions that meet both technical and business requirements.
Neutral selection of existing tools & services on the market.
Strong connection and understanding between IT and specialist departments.
Specialist knowledge from a large number of successful projects.
Strategic improvements that strengthen your position in the market.
Transparent communication and clear targets.
Clear, systematic analysis and implementation for reliable results.
Use of data-driven insights to improve decision-making processes.
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