# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 3 Big Data Enabling the Adoption of Fish Farming Data-Driven Strategies  
The amount of available data does not solely determine the big data value but is determined by how it has been used. Analyzing data from different sources increasesoperational efficiencies, upgrading product development, and driving new revenue and growth prospects by enabling smart decision making. Big data technologies and their applications have been adopted in many fields and have earned one's spurs. Indeed, it offers multiple advantages in terms of detecting the leading causes of failures/anomalies, evaluating the potential risks that may occur, spotting fraudulent situations before it harms the business or increasing the accuracy of artificial intelligence models. Below is a review shedding light on the multiple advantages that big data offers on some domains [10]:  
* Public sector: increasing transparency through accessible, connected data, identifying demands, customizing actions for appropriate products and especially services, decision making with fewer risks, and inventing modern services and products [11]. * Industry: precise demand forecasting, higher supply chain planning, sales support, advanced production operations, and integrating web search-based applications [12; 13]. * Marketing: near real-time clients' behavior analysis based on marketplace collected data, geo-targeted advertising, more effective product design, price and variety optimization, distribution, and logistics management [14]. * Healthcare: clinical decision support systems, personal analytics based on each patient's profile history, personalized treatment, illness pattern analysis, and performance-based payment for medical staff [15; 16]. Putting data at the center of a strategy will provide immense opportunities for organizations to improve [17; 18]. Adopting the same concept in the agricultural domain and especially in the fish farming sector will enhance the domain as a whole by handling multiple parameters (on the farm level as temperature, pH or on the domain level as investing in an area or another) that could tackle the development of the fish farming domain [19]. However, designing a fish farming data-driven strategy can only be possible by implementing a dedicated big data architecture, offering the ability to handle heterogeneous data from different sources. Handling fish farming data is not as easy as it appears, since the objective is to implement a data-driven strategy whose cornerstone is built upon data. Nevertheless, the collected and generated data records have the characteristics of big data (the V's of big data), so handling it is challenging. As a consequence, designing a dedicated data lake architecture is primordial. The data lake remains an adequate data handling platform for the fish farming use case, as it relies on the extract load transform (ELT) process rather than the extract transform load (ETL) process adopted in data warehouse architectures [20]. The ELT process allows ingesting data from heterogeneous sources without undergoing a specific schema and then processing the data to extract hidden patterns and valuable information before exposing it to the end user [21]. The data lake architecture has evolved from mono-zone architecture to multi-zone architecture [22]. With the mono-zone concept, the data lake has a flat architecture where all the collected data is stored in their raw format, with low costs. Despite that, the mono-zone data lake architecture has limited user access and restricted data processing operations. The multi-zone concept usually relies on three main zones, namely, the RZ (raw zone), CZ (conformed zone) and AZ (analytics zone). The RZ is where the data is stored as-is without any transformation; the ingestion can be in batch mode or real-time mode. The main goal of this zone is to provide data engineers with the original version of the data and facilitate subsequent processing. The previously stored data is transformed at the CZ level to match specific needs.