Sample Principles

Overview of the principles from the DT Reference architecture. The implications have to be added in a later phase

Versie 1.0
Creatie datum 02-05-2021

Flexibility of integrating different parallel data sources and providing scalable real time data

Flexibility in terms of integrating different parallel data sources and providing integrated real time data in a scalable way

Data analytics based on data integration layer provides real-time performance and scalability next to batch processing

Data analytics based on data integration layer provides real-time performance and scalability next to batch processing

Data ownership/stewardship and control of the data in the data integration layer.

Data ownership/stewardship and control of the data in the data integration layer. Central management around gathering, integration, and control of data is with the organisation. The single source of truth should include the most possible finest granularity of data.

Scalability with the number of connected devices to provide a basis for real-time analytics

Functionalities plugged in on top of dedicated data integration layer

Functionalities can be developed and plugged in on top of dedicated data integration layer as functional modules or can be sourced from 3rd parties (make or buy decision)

Prioritized capabilities are incubated in a learning environment and OTAP

Prioritized capabilities are incubated in a learning environment to be transitioned effectively in a development, testing, and production environment at a later stage

Flexible customizable analytics functions

Flexible customizable analytics functions (self-defined and customizable algorithms) covering rules-based analytics and machine learning

Auteur Bert Dingemans
Alias
Stereotypes Principle
Details van Flexible customizable analytics functions

Virtualized infrastructure

Virtualized infrastructure to allow easy migration in on-premise and also cloud environments

Auteur Bert Dingemans
Alias
Stereotypes Principle
Details van Virtualized infrastructure

A central design authority governs the data sources and the data integration layers

A central design authority governs the data sources and the data integration layers and design and technology decisions on these layers are with this design authority only

The functional data analytics layer and the data integration layer are coupled by a standard information model

The functional data analytics layer and the data integration layer are coupled by a standard information model that is binding and under governance of the central design authority

Distinguish and decouple classic BI, near real-time BI and real-time OI and prioritize OI real-time requirements.

Distinguish and decouple classic BI, near real-time BI (Business Intelligence), and real-time OI (Operations Intelligence) and prioritize OI real-time requirements.

Cloud and on-premise deployment options

Cloud and on-premise deployment options with ability to migrate from cloud to on-premise (cloud is preferred for short term temporary solution)

Auteur Bert Dingemans
Alias
Stereotypes Principle
Details van Cloud and on-premise deployment options

Data sources on heterogeneous environments

Data sources can be run on heterogeneous environments, several deployment models (on-premise, cloud, etc.) and can be internal and/or external

Auteur Bert Dingemans
Alias
Stereotypes Principle
Details van Data sources on heterogeneous environments

Layered Architecture

Layered architecture that separates data sources, data integration, data analytics, and presentation layers

Auteur Bert Dingemans
Alias
Stereotypes Principle
Details van Layered Architecture

Customizable self-defined API to access platform services

Auteur Bert Dingemans
Alias
Stereotypes Principle
Details van Customizable self-defined API to access platform services

Functional modules and data integration layer can be hosted on different physical environments

Functional modules and data integration layer can be hosted on different physical environments, e.g. the data integration layer is on-premise while the functionalities are plugged in analytics cloud solutions that work on top of the data

Dedicated access via authorization and authentication mechanisms on data integration layer

Ability to abstract on the data sources via implementation of specific connectors

Connection and integration of data sources of different types and ability to abstract on the data sources via implementation specific connectors, e.g. for smart meters, wind craft, etc.