Er is één centrale plek binnen XXX (de centrale D&A omgeving) voor het ontvangen, opslaan, valideren, opwerken, modelleren, integreren en leveren van actuele en historische data en informatieproducten uit verschillende externe en interne (XXX) bronnen en domeinen. De data fundament/data hub is niet bedoeld als doorgeefluik van data. Datafundament voegt waarde toe aan de datastroom. Het centrale datafundament fungeert in deze tweeledig: als ‘data hub” functie en als DWH/dashboard en rapportage functie. Beiden zijn volledig ge-aligned en daardoor maken we gebruik van “een single source of truth” zowel in je planning, als ook het sturen op basis van de realisatie.

Appliance configuration where the EDWH and Big Data functionality is tightly integrated and accessible via various API functions

ETL achtige tool voor transport van Hadoop naar gestructureerde dataopslag zoals RDBMS en DWH

ETL achtige tool voor transport van Hadoop naar gestructureerde dataopslag zoals RDBMS en DWH

This is the logical application of a data warehouse not the component. It describes the functionality of storing, modeling updating and retrieving enterprise data based on relational data including the history of the data

This is a DWH database that acts as data target for the various datapipes. Currently this is the KA-DWH component but for different project implementations there are also other targets possible.

Parallel integration of EDWH and Big Data functionality. Often between these functions an interconnect function is implemented

Serial implementation of Big Data and EDWH functionality where the Big Data functionality is consumed by the EDWH functionality

Implementation of EDWH and Big Data functionality extracted via a virtualisation function. This virtualization acts as an encapsulation layer and API for the consumer applications

Data analytics is een nieuw vakgebied dat door steeds organisaties wordt ingezet. Er zijn vele vormen van data analytics beschikbaar zoals BI, DWH, Predictive Analytics of Machine Learning. Binnen data analytics speelt data modellering een rol. Met name het leggen van verbanden tussen de data entiteiten in de bronnen en het logische model van de analyse is essentieel. In een vroeg stadium nadenken welke modelleervormen relevant zijn, hoe deze aan elkaar verbonden worden. In dit whitepaper hebben we een combinatie van modelleervormen beschreven die een (minimale) set is van generieke notatiewijzen op basis waarvan data analytics.

Whitepaper over dimensioneel datamodelleren ten behoeve van OLAP en DWH modelleren

In the appliance integration of a big data platform with DWH functionality the appliance acts like a black box in which all functionality is integrated in a (proprietary) solution. This solution is configured for optimal performance of transformation and analysis. Characteristics - Appliance is developed, configured and often maintained by an external supplier - It is introduced as a fully integrated solution therefore existing implementations of the DWH have to migrate to this solution - Appliances are often introduced when a cloud solution is selected for the data platform

The parallel integration is an extension of the DWH functiionality with the Big Data Platform. This extension makes it possible to use both functionalities side by side. Characteristics - Easy (incremental) introduction of the Big Data functionality - Integration of both functionalities requires attention for the introduction of the interconnect functionality because this can become a bottleneck in performance and configuration -

Serial integration is implemented by introducing a big data platform for the transformation and extraction of unstructured and semi structured data as source for the EDWH functionality. Characteristics - Introduction of the big data platform is relatively easy since it is an extra layer added to the DWH functionality - Relative easy big data patterns are available because the source is always the datawarehouse - Introducing big data solutions for other functionalities than DHW is not possible.

This integration pattern has a close relation with the parallel integration, however there is an extra layer introduced for the virtualisation and standardisation of data extraction to consumers of the data. Characteristics - Virtualisation layer encapsulate the internal confuguration of the two platforms - The virtualisation layer requires a standardized data or objectmodel for the extraction by the consumers - The virtualisation can become a bottleneck in a number of qualities like performance, integratability e.g. -