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Toward Data Integration in the Era of Big Data
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Houda EL Bouhissi, Archana Patel, Narayan C. Debnath
In recent years, the amount of data generated by physical machines connected to the internet has grown exponentially with time, this complex and large data is called Big Data. It is not only voluminous but also diverse. This heterogenous data creates a problem in storage, analysis, processing, and mainly data integration, which emerges as a challenge for organizations that deploy large data architectures. Therefore, a global approach is primordial to negotiate the challenges of integration. Data integration means “to aggregate data from various sources to provide a meaningful and valuable data sharing” [6]. Traditional data integration tools follow the ETL process that involves Extraction (E), Transformation (T), and Loading (L) of data, depicted in Figure 16.1. ETL aims to load the data from the different sources into a data warehouse. Figure 16.1 shows the traditional data integration process.
Track
Published in Walter R. Paczkowski, Deep Data Analytics for New Product Development, 2020
The process of extracting data from multiple sources, appropriately transforming them, and loading the transformed data into a more convenient data table is referred to as the Extract-Transform-Load process (ETL). This is a standard way of viewing the manipulation of large amounts of data with the goal of making them more accessible and consolidated for the end-user. Once the data have been consolidated, they are ready for the analytical applications. See Lemahieu et al. [2018] for some discussion of the ETL process.2
An Introduction to Business Intelligence
Published in Deepmala Singh, Anurag Singh, Amizan Omar, S.B. Goyal, Business Intelligence and Human Resource Management, 2023
Data warehouses are utilized to combine various types of databases into a central location utilizing a method called ETL as well as standardize these consequences throughout the structure, which enable prospective questions. Data Marts are normally small warehouses that emphasize core databases of a sole department, alternatively gathering information throughout an organization. They confine the complication of information as well as inexpensive to implementing than full warehouses.
BAMN: a modeling method for business activity monitoring systems
Published in Journal of Decision Systems, 2019
Christian Janiesch, Martin Matzner
The modeling of data sources and transformation rules for reports or – at a larger scale – for data warehouses is all about the modeling of the relation of concrete system data to the above set of abstract reference objects (e.g. Bulos, 1996). Models of this type can be used to derive according SQL queries, which manage the data retrieval and processing in batches. The process is called extraction, transformation and loading (ETL). A generic modeling method for ETL has been proposed by Simitsis (2004). However, the applicability of this research to modeling individual BAM KPI is limited since ETL is a batch process and not about real-time monitoring. Nevertheless, it could be employed for the ETL specification of low latency operational data stores as part of a process warehouse.