Our slogan is “Because your data makes sense”.
To make that true we designed a proven Reference Architecture that can be implemented at the speed of the customer and allows the customer to apply the multiple speed implementation.
The Access layer is the central analytical workspace where the data will be explored and analyzed by reports, scorecard and dashboard tools. A trend that is more and more coming up is applications that are using the Enterprise Data Warehouse to explore the data.
The Presentation Layer is the place where the integration of common business logic (calculation, derived values, time series) takes place. Star Schema modelling is preferred here.
This layer can be virtualized instead of persisting it if your database system will allow it.
By using Pivotal Greenplum it’s possible to virtualize the Presentation Layer and reduce the overall project cost by 15%.
In the Foundation Layer integration of data from different source systems will take place and is designed by the principles of Data Vault 2.0.
It consists out of :
- a Raw Data Vault area which is source driven and is generated by our Data Warehouse Automation tool (Foundation Accelerator 4.0). The data is 100% traceable to it’s origin and fully represents the source data at any given point in time.
The Raw Data Vault area reflects the single version of facts and allows you to implement multiple versions of the truth in the Presentation Layer. It’s keeping all requested history of change at atomic level.
- a Business Data Vault area which is Business Driven and is implemented with manually built ELT. Soft business logic changes the data directly or the meaning of the data, such as changing or adding a currency and the associated currency conversion. To improve data retrieval speed, Point in Time (PIT) and Bridge tables are used.
In this area are the enterprise application databases, structured & unstructured data stored. In most cases there is no historical data stored in this layer, that’s why we record all history in the Data Vault Foundation Layer.
This layer also can contain a Data Lake that has the following characteristics:
- High Volume
- High Velocity
The data of the Data Lake is stored on a Hadoop File System or in a NoSQL database. The usage of Big Data SQL to get an optimized access to the data and combine it real time with structured data.
The goals of the Data Lab/ Analytical Area / Sandbox area is :
- To reduce shadow IT : End-Users that will build their own Excels or Ms-Access based reporting.
- To support one of Data Discovery exercises on structured or unstructured data.
- To support Data Mining exercises.
Data inside the Sandbox can be combined from the data warehouse, Big Data “Data Pool” and operational data.
Our Data Integration consultants are specialised in Talend Data Integration.Talend offers robust enterprise data integration software in an open, scalable architecture to respond faster to business requests. Talend provides the unified tools to develop and deploy data integration jobs 10 times faster than hand coding, at 1/5th the cost of competitors.
Master Data Management
Talend Master Data Management (MDM) tools unify all data—from customers to products to suppliers and beyond—into a single, actionable “version of the truth.” Turn your master data into business value with one solution.
Meta Data Management
Accelerate time to compliance and improve data accessibility with detailed information about all of your metadata. Talend Metadata Manager is a metadata management tool that connects data from platforms, databases, and analytics tools to generate a holistic view of the information supply chain in a language that everyone can understand.