# Main Issues in Big Data Security
## 2 Big Data Basis  
The term Big Data refers to a framework that allows the analysis and management of a larger amount of data than the traditional data processing technologies [10]. Big Data supposes a change from the traditional techniques in three different ways: the amount of data (volume), the rate of data generation and transmission (velocity), and the types of structured and unstructured data (variety) [11]. These properties are known as the three basic V's of Big Data. Many authors have added new characteristics to the initial group, such as variability, veracity, or value [12].  
One of the most important parts of the Big Data world is the use of brand new technologies in order to extract valuable information from data and the ability to combine data from different sources and different formats. Big Data have also changed the way in which organisations store data [13], and have allowed them to develop a more thorough and in-depth understanding of their business, which implies a great benefit [14]. Data was traditionally stored in a structured format, such as a relational database, in order to make it easier to process and to understand that data. There is  
Figure 1: Main challenges as regards security in Big Data security, based on [9].  
now, however, a new tendency: storing the current data volumes in unstructured or semi-structured data [15].  
The current maturity of technologies such as Cloud Computing or ubiquitous network connectivity provide a platform on which to easily collect the data, store it, or process it [16]. This set of characteristics has allowed the rapid spread of Big Data techniques. Furthermore, not only can big organisations afford Big Data, but small companies can also obtain benefits from the use of this Big Data ecosystem [17]. A scheme of the typical Big Data ecosystem is shown in Figure 2.  
This success of the use of Big Data technology can be explained by the release of a type of software: Apache Hadoop. Hadoop is a framework developed by Apache that allows the distributed processing of large dataets across clusters of computers using programming models. It is designed to be scalable from a single server to thousands of them, each of which offers computation and local storage [19]. Hadoop can be considered as a de facto standard [20]. The input for the Hadoop Framework is the data that feed the Big Data system. As explained previously, these data usually originate from very different sources and formats. Hadoop has its own distributed file system (HDFS) which stores the data in different servers with different functions, such as NameNode, which is used to store the metadata, or the DataNodes, which store the application data [21]. The principal characteristic of Hadoop is, however, that of being an open-source implementation of MapReduce [22].  
MapReduce is a programming model that is particularly focused on processing and generating large datasets. The MapReduce paradigm accomplishes this goal by describing two different functions [23]:  
* The map function, which processes the key/value pair needed to create a set of intermediate key/value pairs.
* The reduce function, which processes the intermediate values generated and merges them to produce a solution.  
Apart from the MapReduce framework, there are several different projects that comprise the Hadoop ecosystem, such as Hive, Pig, Sqoop, Mahout, Zookeeper, Spark, or HBase. Each of these tools, along with hundreds of others, provides a range of possibilities that allow organisations to obtain value from their data [3].  
Big Data environments do not traditionally prioritise security [24], and it was for this reason that we decided to carry out research in order to discover the main security challenges with respect to Big Data, along with the solutions, methods, or techniques proposed by researchers so as to achieve security in Big Data systems.