The amount and diversity of data that organizations have to deal with, intensify the importance and challenges associated with data management. In this context, data catalogs play a significant role, as they are used as a tool to find and understand data. With the emergence of new approaches to storing and exploring data, such as Data Lake or Data Lakehouse, the requirements associated with building and maintaining the data catalog have evolved and represent an opportunity for organizations to develop their decision-making processes. This work explores a metric data catalog for analytical systems to support building, validating, and maintaining dashboards in a Business Intelligence system.

Abstract Developing analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. Data are a determinant factor in the success of analytical and decision‐making applications, being its nature, availability, and quality, crucial aspects for planning and structuring populating analytical systems. Today's users are more demanding, requiring adaptable and flexible analytical applications, which impose serious challenges on extract‐transform‐load (ETL) systems design and development for ensuring flexible and robust data populating services, operating 24/7, and managing and processing large volumes of data. Thus, we should design and implement ETL processes using innovative and up‐to‐date approaches, having real application evidence. In this paper, we present a service‐oriented implementation for ETL design and development. We mapped and implemented some of the most conventional ETL processes in a service‐oriented architecture, to demonstrate the application and benefits that this kind of approach will provide to ETL systems project development.

One of the most important parts of a Data Warehousing System is the Extract-Transform-Load (ETL) component. It is responsible for extracting, transforming, conciliating, and loading data for supporting decision-making requirements. Usually, due to the complexity of managing heterogeneous data, this component is responsible for consuming most of the resources required for implementing a Data Warehousing System, representing a critical component that compromises the adequacy of the system. Despite their importance, the ETL development method is essentially ad-hoc, which does not always follow or embodies the best practices. With the emergence of Big Data and associated tools, script-based ETL became, even more, a common approach. In the last years, BPMN - Business Process Model and Notation - have been proposed and used to support ETL conceptual models. Still, as an expressive language, it provides different approaches for representing the same requirements. In this paper, we explore the use of BPMN for ETL conceptual modelling, analyzing existing approaches, and proposing a set of guidelines for using this notation in a more consistent way.