The specific features of Big Data i.e., variety, volume, and velocity call for special measures to create ETL data pipelines and data warehouses. A rapidly growing need for analyzing Big Data calls for novel architectures for warehousing the data, such as data lakes or polystores. In both of the architectures, ETL processes serve similar purposes as in traditional data warehouse architectures. ...

The temperature dependence and electronic transport properties of 1, 3, 5-tri(1-phenyl-1H-benzo[d] imidazol-2-yl) phenyl (TPBI) and 8-hydroxyquinoline aluminum (Alq) electron transporting layers (ETL) have been investigated as a function of cesium carbonate (Cs2CO3) doping for organic light emitting devices. The current-voltage and light emission characteristics were measured as a function of t...

OBJECTIVES/HYPOTHESIS To compare the eustachian tube (ET) angle (ETa) and length (ETl) of ears with and without chronic otitis media (COM), and to determine the relationship between ET anatomy and the development of COM. STUDY DESIGN A retrospective case-control study. METHODS The study group comprised 125 patients (age range, 8-79 years; 64 males and 61 females) with 124 normal ears and 12...

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. Research has only recently dealt with the above problem and provided few models, tools and techniques to address the issues around the ETL environment [1,2,3,5]. In this paper, we present a logical m...

Extraction, Transformation and Loading (ETL) processes aim to extract data from data sources to targets, via a set of transformations. In many situations, an ETL process can be subject to changes for several reasons. For instance, data sources changes, new requirements and bug fixing. When changes happen, analyzing the impact of change is mandatory to avoid errors and mitigate the risk of break...

Extract-Transform-Load (ETL) flows periodically populate data warehouses (DWs) with data from different source systems. An increasing challenge for ETL flows is processing huge volumes of data quickly. MapReduce is establishing itself as the de-facto standard for large-scale data-intensive processing. However, MapReduce lacks support for high-level ETL specific constructs, resulting in low ETL ...

Business Intelligence (BI) solutions require the design and implementation of complex processes (denoted ETL) that extract, transform, and load data from the sources to a common repository. New applications, like for example, real-time data warehousing, require agile and flexible tools that allow BI users to take timely decisions based on extremely up-to-date data. This calls for new ETL tools ...

Data warehouse (DW) design is based on a set of requirements expressed as service level agreements (SLAs) and business level objects (BLOs). Populating a DW system from a set of information sources is realized with extract-transform-load (ETL) processes based on SLAs and BLOs. The entire task is complex, time consuming, and hard to be performed manually. This paper presents our approach to the ...