# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 2 Big Data Fundamentals and Elements  
During the past few years, BD technologies have been highlighted as a fundamental and strategic support for very different aspects such as productivity growth, innovation, or customer relation, to name a few. These in turn can strongly benefit large-scale business and sectors, like retailing, manufacturing, healthcare, the public sector, or entire cities [23]. Probably the power of BD is not only in the current availability of extensive data volumes in the organizations, but also in the application of adequate and well-chosen analysis techniques. Working together, these two elements are expected to allow us to gain valuable insight in new applications and markets. According to Gartner's definition [24], BD analytics can actually provide us with three main benefits, namely, valuable insights can be extracted from data, better decisions can be made based on those insights, and decision-making can be automated and included in the management process itself. Current definitions of BD are dependent on the techniques and technologies used to collect, store, process, and analyze available data [15]. BD solutions have been established to usually share several principles [25; 26; 27]. First, they often use high-level architectures, which are necessary to address the different and specific underlying data technologies and sources. Second, they usually include a variety of Data Science tasks, such as Data Mining, Statistical Analysis, ML, Real-Time Visualization, or In-Memory Analysis. Third, they combine the benefits of different tools for different tasks. Fourth, distributed processing and storage is often used across different nodes in a cluster. In addition, finally, coordination among data and processing nodes is ensured to improve scalability, efficiency, and fault-tolerance. The following paragraphs outline the most relevant techniques and technologies which are used today to carry out the design and implementation of BD solutions [15], as they can roughly be grouped into considerations about the BD life cycle, the infrastructural requirements, the frameworks and libraries, and the visualization tools. _(1) The BD Life Cycle._ Dealing with BD implies gathering related knowledge in areas like ML, programming, and databases [15]. Techniques such as sentiment analysis, time series analysis and forecasting, spatial analysis, optimization, visualization, or unstructured analytics (namely, audio and video) are necessities in the BD life cycle for extracting value from it [15; 17], especially when performed in a distributed manner to achieve scalability [26]. This BD life cycle is significantly different from traditional environments, in which data are first explored, then a model is designed, and finally a database structure is created. In BD environments, however, data are first collected and loaded to a certain storage system, then a metadata layer is applied, and finally a structure is created [15]. Therefore, data are first transformed and then loaded in traditional approaches, whilst data are first loaded and then transformed in BD approaches [18; 28]. _(2) Infrastructure Requirements._ An adequate infrastructure is fundamental for providing BD solutions. Among the most relevant are the cloud models, such as the increasingly widespread Infrastructure as a Service (IaaS). In these kinds of models, commodity hardware plays a relevant role, especially due to the lower costs in building shared-nothing architectures [15]. Two BD architectures stand out among so many, namely, the Lambda Architecture [25] and the National Institute of Standards and Technology (NIST) BD Reference Architecture [29]. In order to satisfy particular needs, LambdaArchitecture [25] uses a system based on three main components, namely, a batch layer, a serving layer, and a speed layer (for real-time data).