Big Data Technologies
What Are Big Data Technologies?
Big Data Technologies are scalable hardware and software architectures designed for the parallel processing of massive, complex datasets that traditional systems cannot handle (Vivek Kale et al., 2016). These technologies encompass non-traditional strategies used to collect, organize, and extract insights from data characterized by high volume, velocity, and variety (Sudha Menon et al., 2019). They represent a paradigm shift in computing, evolving from e-commerce and cloud computing to enable the digital analysis of datasets for valuable correlations and causations (Ivan Mistrik et al., 2017).
Architectural Components and Frameworks
The architecture of Big Data Technologies relies on distributed systems and parallel processing to achieve scalability (Vivek Kale et al., 2016). Key software frameworks include the Hadoop ecosystem, which utilizes the Hadoop Distributed File System (HDFS) and MapReduce, and Spark for stream computing and machine learning (Ivan Mistrik et al., 2017), (Shui Yu et al., 2015). These systems must be fault-tolerant and support data partitioning to manage the complexity of unstructured data, such as social media feeds, sensor networks, and digital media (Zhu Han et al., 2017), (Maribel Yasmina Santos et al., 2022).
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Classification of Big Data Systems
Big Data Technologies are categorized into analytic relational systems and non-relational systems (Rajendra Akerkar et al., 2013). Relational systems are optimized for complex processing of structured data, while non-relational systems, including NoSQL and NewSQL databases, handle large amounts of multistructured data like text, audio, and video (Rajendra Akerkar et al., 2013), (Fei Hu et al., 2016). Additionally, these technologies include data mining tools, massively parallel processing (MPP) databases, and cloud computing platforms that facilitate the storage and analysis of diverse data types (Jovan Pehcevski et al., 2023).
Operational Purpose and Impact
The primary purpose of Big Data Technologies is to transform massive amounts of raw data into actionable business intelligence and scientific discovery (Michael Minelli et al., 2012), (Zhu Han et al., 2017). By utilizing advanced algorithms and visualization techniques, these tools allow organizations to improve performance, reduce costs, and enhance decision-making agility (Rajendra Akerkar et al., 2013). They address the challenges of data-intensive computing, enabling the processing of petabytes or exabytes of information to uncover trends that were previously inaccessible due to technological limitations (Shui Yu et al., 2015).