In the era of information, humanity produces huge quantities of data measured in terms of terabytes or petabytes that is yet growing exponentially with time. This situation led to the emergence of a large number of big data systems and technologies that share similar architectures but with different implementations. The common architecture is composed of Data sources, Ingestion, Visualization, Hadoop Platform management, Hadoop Storage, Hadoop Infrastructure, Security, and Monitoring Layers. In our way for a unified abstract implementation, we proposed in a previous work a meta-model for data sources and ingestion layers. We relied on our previous comparatives studies to define key concepts of storage in Big Data to propose a meta-model for storage layer. Thus, in this paper, we are going to present our meta-model for storage layer. The main goal of this universal meta-modeling is to enable Big Data distribution providers to offer standard and unified solutions for a Big Data system.

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Big data is about collecting, storing, managing, processing massive quantities of daa, but it is also about presenting various insights into the collected data through visualization. Visualization layer is located on the top of a layered architecture composed of Data Sources, Ingestion, Storage, Management, Monitoring, and Security layers. While each of these layers has its own challenges, visualization is presenting a particular challenge because of physical limitations of the display area and the chosen mode of data presentation. Along with the visualization modes, OLAP is a powerful technology for data discovery, including capabilities for limitless report viewing, complex analytical calculations, predictive scenario planning, and visualization. Based on our previous comparative studies in which we identified key concepts of visualization layer of major Big Data distributions, we propose in this paper to map this layer to OLAP visualization technology. To achieve this goal, we apply techniques related to Model Driven Engineering "MDE" to propose a universal meta-model for visualization layer in a Big Data systems, in which we integrated a meta-model of OLAP for data presentation.

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Nowadays, the magnitude of data generated daily through the technological environment has increased enormously. This massive amount of heterogeneous data led to the emergence of a large number of big data systems and technologies that share similar architectures but with different implementations. In essence, the common architecture is composed of many components: Data sources, Ingestion, Hadoop Storage, Hadoop Platform management, Visualization, Monitoring, and Security Layers. In our way for a unified abstract implementation, we proposed, in previous works, meta-models for data sources, ingestion, storage, and visualization layers. We also relied on our previous comparatives studies to define key concepts of management layer in Big Data. Thus, we shall present in this paper our meta-model for management layer in Big Data by applying techniques related to Model Driven Engineering. The main goal of this universal meta-modeling is to enable Big Data distribution providers to offer standard and unified solutions for a Big Data system.