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Each day huge quantities of data are generated from digital technologies and information systems. Therefore, processing these massive data requires a specific architecture and a good knowledge on how to handle data. Traditional databases management system can no longer be used for this type of data since they were originally designed for limited and structured data. Moreover, dedicated architecture known as Data Lake has been developed in order to extract valuable information hidden in data. The main objective of this paper is to explore the two architectures, namely, data warehouse and data lake. Furthermore, it describes the main differences and exposes key factors of each one. IntroductionIn former times, the computer programs were focused mainly on algorithms and programming languages, so processing data as names, addresses, phone numbers… was not a priority. However, since the computers started to become commercially available and while the business people started using them for real-world cases, the data suddenly became very important.Accordingly, researchers started proposing many databases management systems (DBMS), and as result, one of the first DBMS is the Navigational Databases by Charles Bachman [1] in the mid-1960, it was based on the CODASYL approach [2], which rely on manual navigation technics using a linked data set, forming a huge network. However, this DBMS was a very complicated system and required specific

Thanks to continuously evolving data management solutions, data-driven strategies are considered the main success factor in many domains. These strategies consider data as the backbone, allowing advanced data analytics. However, in the agricultural field, and especially in fish farming, data-driven strategies have yet to be widely adopted. This research paper aims to demystify the situation of the fish farming domain in general by shedding light on big data generated in fish farms. The purpose is to propose a dedicated data lake functional architecture and extend it to a technical architecture to initiate a fish farming data-driven strategy. The research opted for an exploratory study to explore the existing big data technologies and to propose an architecture applicable to the fish farming data-driven strategy. The paper provides a review of how big data technologies offer multiple advantages for decision making and enabling prediction use cases. It also highlights different big data technologies and their use. Finally, the paper presents the proposed architecture to initiate a data-driven strategy in the fish farming domain.

As the global population increases rapidly, so does the need for fishing products. Aquaculture is well-developed in Asian countries but is underdeveloped in countries that share Morocco's climate. To meet the rising demands for aquaculture production, it is vital to embrace new digital strategies to manage the massive amount of data generated by the aquaculture environment. By employing Big Data methodologies, aquaculture activity is handled more effectively, resulting in increased production and decreased waste. This phase enables fish farmers and academics to obtain valuable data, increasing their productivity. Although Big Data approaches provide numerous benefits, they have yet to be substantially implemented in agriculture, particularly in fish farming. Numerous research projects investigate the use of Big Data in agriculture, but only some offer light on the applicability of these technologies to fish farming. In addition, no research has yet been undertaken for the Moroccan use case. This study aims to demonstrate the significance of investing in aquaculture powered by Big Data. This study provides data on the situation of aquaculture in Morocco in order to identify areas for improvement. The paper then describes the adoption of Big Data technology to intelligent fish farming and proposes a dedicated architecture to address the feasibility of the solution. In addition, methodologies for data collecting, data processing, and analytics are highlighted. This article illuminates the possibilities of Big Data in the aquaculture business. It demonstrates the technological and functional necessity of incorporating Big Data into traditional fish farming methods. Following this, a concept for an intelligent fish farming system based on Big Data technology is presented.

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