Big Data systems are often composed of information extraction, preprocessing, processing, ingestion and integration, data analysis, interface and visualization components. Different big data systems will have different requirements and as such apply different architecture design configurations. Hence a proper architecture for the big data system is important to achieve the provided requirements. Yet, although many different concerns in big data systems are addressed the notion of architecture seems to be more implicit. In this paper we aim to discuss the software architectures for big data systems considering architectural concerns of the stakeholders aligned with the quality attributes. A systematic literature review method is followed implementing a multiple-phased study selection process screening the literature in significant journals and conference proceedings.


Various industries are facing challenges related to storing and analyzing large amounts of data. Big Data Systems become nowadays a very important driver for innovation and growth, by means of the insights and information that is obtained via the excessive processing of data. The business and application requirements vary depending on the application domain. Software architectures of big data systems have been previously studied sporadically/extensively. However, it is not easy to suggest a suitable software architecture for big data systems, when considering also both the application requirements and the stakeholder concerns [1].

The interactions and relations among the elements and all the elements as a whole that are necessary to reason about the system define the architecture of that system [2].. The architecture is constructed considering the driving quality attributes therefore it is important to capture those and analyze how these are satisfied by an architecture [3]. The requirements that are satisfied with the given architecture shall also match with the quality attributes.

In this study, we provide a systematic literature review (SLR) focused on the Software Architectures of the Big Data Systems in terms of the application domain, architectural viewpoints, architectural patterns, architectural concerns, quality attributes, design methods, technologies and stakeholders. The challenging part of the study was screening the publications from various domains. The variety of the application areas of big data systems brings along the dissimilar representations of the system architectures with flexible terminologies.

In order to achieve the requirements provided by different stakeholders which derive different architectural configurations, a proper architectural design with consistent terminology is essential. We aim to focus on the software architectures for big data systems considering architecture design configurations derived by architectural concerns of the stakeholders aligned with the quality attributes which are implicit in design of various systems.

The application areas of the big data systems vary from aerospace to healthcare [45], and depending on the application domain, the functional and non-functional concerns vary accordingly, influencing both the architectural choices and the implementation of big data systems. To shed light on the experiences reported in the recent literature with deploying big data systems in various domain applications, we conducted a systematic literature review.

Our aim was to consolidate reported experience by documenting architectural choices and concerns, summarizing the lessons learned and provide insights to stakeholders and practitioners with respect to architectural choices for future deployment of big data systems.

The study aims to investigate the big data software architectures based on application domains assessing the evidence considering the interrelation among the data extraction area and the quality attributes with the systematic literature review methodology which is the suitable research method. Our research questions are derived to find out in which domains big data is applied, the motivation for adopting big data architectures and to identify the existing software architectures for big data systems We identified 622 papers with our search strategy. Forty-three of them are identified as relevant primary papers for our research. In order to identify various aspects related to the application domains, we extracted data for selected key dimensions of Big Data Software Architectures, such as current architectural methods to deal with the identified architectural constraints and quality attributes.

We presented the findings of our systematic literature review to help researchers and practitioners aiming to understand the application domains involved in designing big data system software architectures and the patterns and tactics available to design and classify them.

Big data

The term “Big Data” usually refers to data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. In general, Big Data can be explained according to three V’s: Volume (amount of data), Velocity (speed of data), and Variety (range of data types and sources). The realization of Big Data systems relies on disruptive technologies such as Cloud Computing, Internet of Things and Data Analytics.

With more and more systems utilizing Big Data for various industries such as health, administration, agriculture, defense, and education, advances by means of innovation and growth have been made in the application areas. These systems represent major, long-term investments requiring considerable financial commitments and massive scale software and system deployments.

The big data systems are applicable to the data sets that are not tolerable by the ability of the generic software tools and systems [6]. The contemporary technologies within the area of cloud computing, internet of things and data analytics are required for the implementation of the big data systems. Such massive scale systems are implemented using long term investments within the industries such as health, administration, agriculture, defense and education [7].

Big data systems analytic capability strongly depends on the extreme coupling of the architecture of the distributed software, the data management and the deployment. Scaling requirements are the main drivers to select the right distributed software, data management and deployment architecture of the big data systems [8]. Big data solutions led to a complete revolution in terms of the used architecture, such as scale-out and shared-nothing solutions that use non-normalized databases and redundant storage [9].

As a sample domain, space business already benefits from the big data technology and can continue improving in terms of, for instance horizontal scalability (increasing the capacity by integrating software/hardware) to meet the mission needs instead of procuring high end storage server in advance. Besides multi-mission data storage services can be enabled instead of isolated mission-dedicated warehouse silos.

Improved performance on data processing and analytics jobs can support activities such as early anomaly detection, anomaly investigation, and parameter focusing. As a result, big data technology is transforming data-driven science and innovation with platforms enabling real-time access to the data for integrated value.

The trend is to increase the role of information and value extracted from the data by means of improving the technologies for automatic data analysis, visualization and use facilitating machine learning and deep learning or utilizing the spatio-temporal analytics through novel paradigms such as datacubes.

Systematic reviews

The systematic literature review is a rigorous activity that is applied screening the identified studies and evaluating such studies based on the defined research questions, topic areas or phenomenon of interest. As a result of the evidence gathered for a particular topic, the gaps can be investigated further with supporting studies.

Evidence-based research is successfully conducted initially in the field of medicine and similar approaches are adopted in many other disciplines. Among the goals of the evidence-based software engineering, the quality improvement, assessing the application extent of the best practices for the software-intensive systems can be listed. Besides the evidence based guidelines can be provided to the practitioners as a result of such studies. Considering the benefits of the evidence based research, its application is valuable also in the software engineering field.

The systematic literature review shall be transparent and objective. Defining clear inclusion/exclusion criteria for the selected primary studies is critical for the accuracy and consistency of the output of the review. Well defined inclusion/exclusion criteria minimizes the bias and simplifies the integration of the new findings.

Software architectures

The software architecture is the high-level representation and definition of a software system providing the relationships between architectural elements and sub-elements with a required level of granularity [310]. Views and beyond is one of the approaches to define and document software architectures [11]. Viewpoints are generated to focus on relevant quality attributes based in the area of use for the stakeholder and more than one viewpoint can be adopted depending on the complexity of the defined system. In order to solve common problems within the architecture, architectural patterns are designed within the relevant context. Architectural patterns, templates, and constraints are consolidated and described in viewpoints.

The research questions are defined using the objectives of the systematic review as discussed in section 3.2 which is followed by drawing the scope (time range and publication resources) and the strategy (section 3.3). The search strategy is shaped by conducting pilot searches to form the actual search strings.

The appropriate definition of the search string reduces the bias and helps to achieve the target precision. The inclusion/exclusion criteria (section 3.4) is defined as the next step. The primary studies are filtered applying the inclusion/exclusion criteria. The success of the study selection process is assessed via the peer reviews of the authors.

The selected primary studies are passed through a quality assessment (section 3.5). Afterward, the data extraction strategy is built to gather the relevant information from the selected set of studies (section 3.6). The data extraction form is constructed and filled with the corresponding output to present the results of the data synthesis.

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