In this work, we present a Metadata Framework in the direction of extending intelligence mechanisms from the Cloud to the Edge. To this end, we build on our previously introduced notion of Data Lagoons—the analogous to Data Lakes at the network edge—and we introduce a novel architecture and Metadata model for the efficient interaction between Data Lagoons and Data Lakes. We identify the service and data planes of our architecture and we illustrate the application of our framework on a use case from the TPCx-IoT benchmark. To our knowledge, our approach is the first one to examine the integration of Data Lakes with Edge components, taking under consideration data and infrastructure resources of Edge Nodes.@en

Data Warehouses (DW) have to continuously adapt to evolving business requirements, which implies structure modification (schema changes) and data migration requirements in the system design. However, it is challenging for designers to control the performance and cost overhead of different schema change implementations. In this paper, we demonstrate SCIT, a tool for DW designers to test and implement different logical design alternatives in a two-fold manner. As a main functionality, SCIT translates common DW schema modifications into directly executable SQL scripts for relational database systems, facilitating design and testing automation. At the same time, SCIT assesses changes and recommends alternative design decisions to help designers improve logical designs and avoid common dimensional modeling pitfalls and mistakes. This paper serves as a walk-through of the system features, showcasing the interaction with the tool’s user interface in order to easily and effectively modify DW schemata and enable schema change analysis.@en