# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 1 Introduction  
Nowadays, big data and its applications are ubiquitous since businesses and organizations have increasingly adopted data-driven strategies which allow them to tackle many challenges [1]. In parallel, cloud computing and high-performance computing became increasingly accessible as a service, which made adopting big data solutions in many domains possible [2]. However, despite all the advantages of adopting a data-driven strategy, the agricultural domain has not benefitted sufficiently from it yet, especially in the fish farming field [3]. Moreover, compared to other domains, the agricultural field, in general, and fish farming, in particular, has a high level of uncertainty due to various parameters that affect the quality and quantity of production [4]. Consequently, applying a data-driven strategy in the fish farming domain will not only bring benefits to farmers but also enhance the sector as a whole. The proposition to adopt a strategy for the whole domain instead of focusing on the fish farms specifically has not yet been discussed and the proposition of an architecture to initiate this strategy has not been addressed either. This assertion is a result of the literature and the more detailed comprehensive review is in the methodology section. A data-driven strategy for fish farming will reduce uncertainties and provide continuous decision support, revolutionizing the fish farming sector [3]. Nevertheless, making the data the pillar of this strategy will directly impact the production efficiency and nutrition quality with minimal environmental damages, which implies social, economic, and ecological benefits [5]. To obtain crucial information about massive datasets, advanced data analysis techniques have been used to transform big data into valuable data since it helps organizations and businesses make better decisions. For example, in the field of healthcare analytics, large datasets (provided by applications such as electronic health records and clinical decisionsystems) enable healthcare practitioners to provide effective and accurate solutions for patients based on their overall history rather than relying only on the current collected data [6]. It has to be noted that traditional data analytics are deprecated to use in a big data context as long as the V's that characterize big data, namely, veracity, value, velocity, volume, and variety will immediately affect the accuracy of results. Additionally, other characteristics may be added to big data such as viscosity, venue, validity, and viability [7]. Furthermore, many artificial intelligence (AI) methods were developed to extract valuable information and discover hidden patterns from enormous amounts of data more accurately and faster than traditional techniques [8], such as machine learning (ML) or data mining (DM). For instance, a detailed analysis of historical fish farming data, real-time collected data, and other parameters will provide a detailed and extensive vision of the fish farming market status, thereby taking the appropriate decisions regarding the fish farms management [9]. Fish farming data have three main sources: sensors, flat files, and APIs. The parameters collected from sensors mainly are the following: temperature, light, chemical composition, and average weight of fish. Flat files contain manual data that cannot be automated or has yet to be automated like food quantities or fish count. Furthermore, the APIs provide another type of data like weather data, fish prices, fish food prices, and others. This article is organized into seven sections: First, an introduction to present big data applications and how they can be used to enhance domains. Second, we present the methodology used in this study. After that, in Section 3 we highlight the advantages of adopting data-driven strategies in several fields and how it enables the adoption of fish farming data-driven strategies. Section 4 presents the different big data technologies used for proposing a dedicated data lake architecture.