Advanced analytical techniques and sophisticated decision-making strategies are imperative for handling extensive volumes of data. As the quantity, diversity, and speed of data increase, there is a growing lack of confidence in the analytics process and resulting decisions. Despite recent advancements, such as metadata mechanisms in Big Data Processing and Systems of Deep Insight, effectively managing the vast and varied data from diverse sources remains a complex and unresolved challenge. Aiming to enhance interaction with Data Lakes, this paper introduces a framework based on a specialized semantic enrichment mechanism centred around data blueprints. The proposed framework takes into account unique characteristics of the data, guiding the process of locating sources and retrieving data from Data Lakes. More importantly, it facilitates end-user interaction without the need for programming skills or database management techniques. This is performed using Digital Twin functionality which offers model-based simulations and data-driven decision support.

In the continuously evolving and growing landscape of Big Data, a key challenge lies in the transformation of a Data Lake into a Data Mesh structure. Unveiling a transformative approach through semantic data blueprints enables organizations to align with changing business needs swiftly and effortlessly. This paper delves into the intricacies of detecting and shaping Data Domains and Data Products within Data Lakes and proposes a standardized methodology that combines the principles of Data Blueprints with Data Meshes. Essentially, this work introduces an innovative standardization framework dedicated to generating Data Products through a mechanism of semantic enrichment of data residing in Data Lakes. This mechanism not only enables the creation readiness and business alignment of Data Domains, but also facilitates the extraction of actionable insights from software products and processes. The proposed approach is qualitatively assessed using a set of functional attributes and is compared against established data structures within storage architectures yielding very promising results.

One of the greatest challenges in Smart Big Data Processing nowadays revolves around handling multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data through Data Lakes. The latter requires a disciplined approach to collect, store and retrieve/analyse data to enable efficient predictive and prescriptive modelling, as well as the development of other advanced analytics applications on top of it. The present paper addresses this highly complex problem and proposes a novel standardization framework that combines mainly the 5Vs Big Data characteristics, blueprint ontologies and Data Lakes with ponds architecture, to offer a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval from a Data Lake. The proposed mechanism is compared qualitatively against existing metadata systems using a set of functional characteristics or properties, with the results indicating that it is indeed a promising approach.