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The Open Business Data Lake Standard, Part V

Blog: Capgemini CTO Blog

In my previous blog posts (Part I,  Part II,  and Part III) about the ‘Open Business Data Lake Conceptual Framework (O-BDL) I introduced its background, concept, characteristics and platform capabilities. In Part IV) I compared a Data Lake with other data processing platforms. In this fifth part I’ll discuss the key concepts of an O-BDL and describes how it should work.

To understand how an O-BDL should work, the following process diagram (applying The ArchiMate® Enterprise Architecture Modeling Language) is used.

An O-BDL ingests data (batch, micro-batch  as well as real-time) from other applications (through feeds) and data sources (through ingestion). Most data items (structured and semi- and unstructured) go straight into the main distributed – thus scalable – data store preserving the original structure. Some data items, however, are processed on arrival since the resulting insight is needed immediately. Data stored in an O-BDL is called data-at-rest and data that is processed without storing is called data-in-motion.

An O-BDL can generate insights from different assemblies of the ingested data. These insights can be translated into actions in multiple ways that make sense from a business point of view. A service layer takes care of delivering insights at different points of action into business processes, applications, etc.

To turn data into insights an O-BDL employs two types of data processing capabilities:

Different functions will manage the O-BDL and use of its content . The key roles involved in an O-BDL:

Now it’s clear how an O-BDL should work, it’s time to define possible business scenario’s which can make use of an O-BDL. This will be discussed in the sixth blog post.

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