## 2 Background and Related Work  
Since the term \"big data\" first appeared in the scientific literature, it has been a dynamic industry closely related to human resources and a significant source of employment [13]. The requirement for expertise in big data, which has emerged as a natural consequence of the widespread use of big data, has been a fundamental issue frequently emphasized in the literature, particularly over the past decade [14]. Recent research indicates that, unlike traditional data proficiency, big data-driven services and applications require more advanced and specialized professional knowledge and skills.  
Numerous synchronized processes based on storage, processing, analytics, and visualization comprise big data workflows [2]. Services and applications based on real-time data processing and analysis utilizing dynamic data flows have spawned new specializations for big data. Notable big data applications include online analytical processing (OLAP), streaming analytics, business intelligence, and business data analytics [15]. Consequently, meeting the growing demand for skilled labor in the big data industry has emerged as a problem that must be addressed in the near future [12]. This issue has been identified in the literature as a big data skills gap and has been highlighted in numerous scientific studies and industry reports [8; 12; 14]. On the other hand, a limited number of specific studies have been conducted based on the analysis of job postings to reveal the required knowledge and skills in the big data industry [5]. The focus of these studies was on various aspects of big data competency requirements, including big data analytics, data science, and other big data specializations [3; 6; 8; 16].  
Among them, the study by [PERSON] et al. (2014) is one of the most prominent. They compiled a dataset with 1357 business intelligence and 450 big data job postings [3]. They analyzed business intelligence and big data job postings using text mining procedures and the latent semantic analysis (LSA) technique. [PERSON] et al. (2018) analyzed 2786 job postings related to big data utilizing the latent Dirichlet allocation (LDA) method in a separate study [4]. They identified four roles (business analysts, data scientists, developers, and system administrators) and nine skills that correspond to these roles. Using text mining techniques, [PERSON] et al. (2018) analyzed 1216 big data job postings [8]. In a similar study, [PERSON] et al. (2018) surveyed 1050 online job postings using semantic content analysis to identify the knowledge domains and skill sets that distinguish data scientist and data analyst positions [16].  
[PERSON] (2019) analyzed 2175 big data job postings using latent Dirichlet allocation (LDA), a generative method for topic modeling, and identified 60 topics for big data competencies [17]. Using the LDA-based topic modeling technique, [PERSON] and [PERSON] (2019) analyzed 2638 job postings and revealed the big data skills required for software engineering [5]. Using text mining techniques, [PERSON] (2020) analyzed 3009 job postings for big data analytics. This study found that cognitive and functional skills, as well as social and meta-skills, are necessary for big data analytics [10]. Using the LSA method, [PERSON] et al. (2022) conducted a study on big data and data science skills by analyzing 1200 job postings. As a result, they determined the connections between academic and sectoral perspectives [6].  
These studies, which investigate the various contexts of big data careers, have revealed the knowledge domains and skill sets required for big data professionals. The studies aimed to close the talent gap in the big data industry, benefiting both industry and academia. In this regard, our study on the competency requirements for big data extends the methodology and findings of the previous research.