Extract, Transform And Load Tools Research Articles

CLINICAL DATA WAREHOUSE A REVIEW

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers in the clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is an important solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a central repository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinical decisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools that view the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics; requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach; architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholar are used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. These papers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to find insights related to aspects of CDW. This review can contribute answers to questions related to CDW and provide recommendations for implementing a successful CDW.

CLINICAL DATA WAREHOUSE: A REVIEW

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW.

Managing application compatibility during ETL tools and environment upgrades

Extract, transform and load (ETL) tools are software applications responsible for data extraction from multiple sources, cleansing, customisation and insertion into a data warehouse. ETL tools integrate data, which enables the building of the enterprise data warehouse. To meet different business requirements and in order to bring a system up to date or to improve its data integration capabilities, it is necessary to upgrade ETL tools periodically. This paper reviews issues related to software and tools upgrades in data warehousing environments. It reads as a research report for a practitioner audience. We propose the managing of ETL application compatibility during software upgrades through automated tools and processes. We will discuss the challenges involved in ETL application compatibility evaluation during ETL tool upgrade and propose step-by-step methods to approach this. We also share suggestions and recommendations for improvement realised through recent upgrade activities conducted in an enterprise data warehouse production environment.

Data Analytics and Operational Data Integration to reach out to Rural Masses for Early Detection of Non-communicable Diseases

This paper demonstrates the analysis of healthcare data and integration of operational data to abate the prevalence incidence of non-communicable diseases (NCD). Pilot experiments have been carried out in Suttur village, by screening the masses for early detection of NCDs. An app has been developed to record patient profile onto a tablet. The record is synchronized to update the database on the cloud where the repository is maintained. This provides an efficient way of analysis and statistics to the huge amount of health data. ETL (Extract, Transform and Load) is a process used to extract data from data repository and transform the data based on user requirements and store in a target database as a single repository which helps to achieve goals proactively and on time. The main aim of this paper is to generate reports from the collection of health data by using tools like QlikView for Business Objects as a front end tool for generation of reports and charts, Oracle 11g as backend tool for creation of data repository and Talend Open Studio 5.4 as an ETL tool. We conclude that ETL systems enable a smooth migration from one system to another. By creating an ETL script for each system, data can be stored in a consistent format in the repository. The source system can then be changed, without any impact on the repository or the reporting/analysis systems. Therefore there are phenomenal improvements in turnaround time for data access and reporting. The entire health data can be standardized as there will be one view of information. Health data synced by various sources from different places can be merged to create a more comprehensive information source. This leads to reduction in costs to create and distribute information and reports and also helps in reduction of prevalence incidence of NCDs.

Prototype of a Web ETL Tool

Extract, transform and load (ETL) is a process that makes it possible to extract data from operational data sources, to transform data in the way needed for data warehousing purposes and to load data into a data warehouse (DW). ETL process is the most important part when building the data warehouse. Because the ETL process is a very complex and time consuming, this paper presents a prototype of a web ETL tool that offers step-by-step guidance through the entire process to the end user. This ETL tool is designed as a web application so users can save time (and space) required for installation purposes.

Revised Framework for ETL Workflow Management for Efficient Business Decision-Making

Business decision-making is not a simple task. There are many reasons for that but the main reason is data comes from heterogeneous operational sources of an organization. Therefore, it is difficult to organize and maintain especially if a huge volume of data is involved. A data warehouse is helpful in this regard as it can assists in business decision-making. Data collection and loading it into a data warehouse is difficult job because data sources are not in consistent form. This job usually consists of three main processes that involve extraction, transformation and loading. To extract the data from different sources, then transform it into a unified format and consequently load it into the warehouse, ETL (Extract, transform and load) tools are required. Nowadays, the majority of ETL tools organize workflow. An ETL workflow can be considered as a group of ETL jobs with dependencies between them. In this research paper a revised ETL workflow management framework which is based upon different considerations is proposed. These considerations along with the addition of the components in the workflow scheduling layer would help in making more effective and quality business decisions.