ETL

In today's society, an enormous amount of data is created that is stored in various databases. Since the data is in many cases stored in different databases, there is a demand from organizations with a lot of data to be able to merge separated data and get an extraction of this resource. Extract, Transform and Load System (ETL) is a solution that has made it possible to easily merge different databases. However, the ETL market has been owned by large actors such as Amazon and Microsoft and the solutions offered are completely owned by these actors. This leaves the consumer with little ownership of the solution. Therefore, this thesis proposes a framework to create a component based ETL which gives consumers an opportunity to own and develop their own ETL solution that they can customize to their own needs. The result of the thesis is a prototype ETL solution that is built with the idea of being able to configure and customize the prototype and it accomplishes this by being indep...

The paper analyses the problem of data cleaning and automatically identifying the “incorrect and inconsistent data” in the dataset. Extraction, Transformation and Loading (ETL) are the different steps for cleaning a data warehouse. Authors have implemented different algorithms like: cleanString, cleanNumber, hit ratio, check data dictionary, check metadata etc in addition to various existing data cleaning algorithm like PNRS. This paper tries is to improve the quality of data in the database system. This paper emphasizes on the citizen database system to make it errorless. Some of the results along with certain statistics are also provided here.

Data warehouse is used to provide effective results from multidimensional data analysis. The accuracy and correctness of these results depend on the quality of the data. To improve data quality, data must be properly extracted, transformed and loaded into the data warehouse. This ETL process is the key to the success of a data warehouse. In this paper we propose a conceptual ETL framework for an object oriented data warehouse design, the framework is called UCLEAN. This framework takes into account the concept of requirements of the users .The data is extracted from different UML sources and is converted into a multidimensional model. It is then cleaned and loaded in the data warehouse. We validate the effectiveness of the framework through a case study.

Abstract. Satellite data are used in several environmental applications, particularly in air quality supervising, climate change monitoring, and natural disaster predictions. However, remote sensing (RS) data occur in huge volume, in near-real time, and are stored inside complex structures. We aim to prove that satellite data are big data (BD). Accordingly, we propose a software as an extract-transform-load tool for satellite data preprocessing. We focused on the ingestion layer that will enable an efficient RSBD integration. As a result, the developed software layer receives data continuously and removes ∼86  %   of the unused files. This layer also eliminates nearly 20% of erroneous datasets. Thanks to the proposed approach, we successfully reduced storage space consumption, enhanced the RS data accuracy, and integrated preprocessed datasets into a Hadoop distributed file system.

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students' academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students' degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-tolong term academic decisions.

The paper presents a mapping-based and metadata-driven modular data transformation framework designed to solve extract-transform-load (ETL) automation, impact analysis, data quality and integration problems in data warehouse environments. We introduce a declarative mapping formalization technique, an abstract expression pattern concept and a related template engine technology for flexible ETL code generation and execution. The feasibility and efficiency of the approach is tested on the pattern detection and data lineage analysis using large real life SQL corpuses.

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.

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In this paper we have discussed the problems of data quality which are addressed during data cleaning phase. Data cleaning is one of the important processes during ETL. Data cleaning is especially required when integrating heterogeneous data sources. This problem should be addresses together with schema related data transformation. At the end we have also discussed the Current tool which supports data cleaning.

The organizations always need to manage their operations, process the data electronically, and find a platform that help them to support their strategic decisions. The success of the human resource department can reflect overall organizational success. The human resource department professionals try to ensure finding the right person at the right time for the job that fits the person according to skills and qualifications. This task needs a platform that supports making the right decision based on historical managerial information. The data mart is a departmental based decision support system that used departmental data to help decision-makers to support short term decisions. Human resource (HR) data mart is the base stone for building an enterprise data warehouse. The paper presents the implementation process of HR data mart starting from implementing data mart schema to online analytical processing (OLAP) reports. The data mart is implemented on retired employees' data of Basra Oil Company for over 15 years. A human resource data mart can provide a base platform to perform a different analysis operation to support the right decisions. Different OLAP reports are implemented to help analysts and decision-makers to get the answers for their questions as OLAP queries. Two categories of reports are implemented offline reports using Microsoft Excel Pivot Table 2010 and web OLAP reports using SQL Server Reporting Service 2014 (SSRS). The tools used to implement data mart vary from SQL Server Management Services (SSMS) 2014, SQL Server Integration Service 2014 (SSIS), SQL Server Analytical Service 2014 (SSAS), SQL Server Reporting Service 2014 (SSRS), SQL Server Data Tools 2013 (SSDT), and Microsoft Excel Pivot Table 2010.

Volume and complexity of data collected in datawarehouse systems is growingrapidly. This is posing challenges to traditional datawarehouse platforms. At the same time, Hadoop ecosystem has opened new avenues for implementing datawarehouse systems on Hadoop and overcome these challenges. In this paper we survey previous studies about limitations of traditional datawarehouse platforms. Opportunities offered by Hadoop for datawarehouse implementation are discussed. This paper can give a direction to future research in the areas of Datawarehouse implementation on Hadoop platform.

In this pdf file we will discuss how to use etl tools software easily.

Softwware etl tools are very easy to use. In ETL Tools we can import or export into different files, such as excel files, mysql files and tableau files.

https://www.etl-tools.com/advanced-etl-processor-enterprise/overview.html

https://www.etl-tools.com/wiki/aetle/start?do=export_pdf

International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.4, November 2011 DOI : 10.5121/ijcses.2011.2404 45 UCLEAN: AREQUIREMENT BASED OBJECT-ORIENTED ETL FRAMEWORK Payal Pahwa 1 , Shweta Taneja 2 and Garima Thakur 3 ...

As the airline company in Indonesia which is the one of domestic leading airlines, Sriwijya Air has been created the electronic voucher called e-voucher Sriwijaya Air for loyal customers. The e-voucher is used as a flight ticket payment instead of cash payment to buy a ticket. The e-voucher consists of some attributes such as destination, flown date, and flight class. Particularly, a marketing manager needs to review the utilization of e-voucher when a ticket has been approved in every year. The proposed solution is to redeploy existing OLTP database into data warehouse. The data warehouse already developed using nine-step Kimball's methodology. All data will be analyzed using OLAP to present a report in a visual form such as dashboard. Furthermore, airlines company can produce an analysis report using business intelligence.

The quality is the key concept in each and every analysis as well as in computing applications. Today we gather large volumes of information and store them in multidimensional mode that is in data warehouses then analyze the data to be used in exact decision making in various fields. The studies proved that most of the volumes of data are not useful for analysis due to lack of quality caused by improper data handling techniques. This paper try to find out a solution to achieve the quality of data from the foundation of data repositories and try to avoid quality anomalies at meta data level. This paper also proposes the new model of Meta data architecture.

Here is a draft of a proposal for a crypto token concept. The short version is that the token's quote would be a realtime mirror of our capacity to ensure our growth and survival odds as a specie. This capacity could become an objective asset built from underlying positive and negative indicators as well.

ETL is a process for data integration to provide a consolidated view of data that involves three steps such as Extract, Transform and Load to combine data from multiple sources. In this process, data is fetched from numerous data sources, transformed into a particular format and finally loaded into a suitable data warehouse. Developers have to write the Scala code using Spark SQL to perform ETL operations. But as and when the requirement changes, the developers have to write code again accordingly. This paper presents an idea to build a tool to facilitate ETL process using Spark. The tool will automatically generate Scala scripts from the uploaded ETL mapping document. In order to verify the scripts, the system also provides unit test cases and SQL queries using Spark SQL. This will minimize the repetitive tasks faced by developers and provide a robust system which will ease the development of ETL operations.

One of the best ways to enhance the performance of all companies and manage Customer Satisfaction is to get the consumers' complaints and analyze them in order to fix them. These complaints represent the consumers' behavior to the companies and how these company's response to them. Besides, customers' satisfaction is the main goal of all companies and this goal cannot achieve if they do not handle the customers' complaints. The paper represents a framework of complaint data mart construction where the source data are thousands of complaints about services and financial products of companies. The data mart represents the first step to implement an enterprise data warehouse (DW) to support strategic decisions. Reports are constructed to help analysts and decision-makers to support their decisions related to consumers' complaints and how to improve service quality. Two different categories of on-line analytical processing (OLAP) reports are used, offline and web OLAP reports. The two types of reports provide a deep view of the data and present the analysts with flexible charts that can be used in supporting strategic decisions. SQL Server Management Studio (SSMS), SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS) 2014 beside SQL Server Data Tools (SSDT) 2013 is used to build the data mart staging table, schema, cube, and OLAP reports. MS Excel Pivot table 2010 is used also to import the cube and build offline reports and implementing OLAP processes. This data mart can be utilized by consumers themselves besides decision-makers and analysts. The data mart can measure how the companies fix complaints issues and prevent them from occurring again and identify the factors that influence financial customers' satisfaction.

The organizations always need to manage their operations, process the data electronically, and find a platform that help them to support their strategic decisions. The success of the human resource department can reflect overall organizational success. The human resource department professionals try to ensure finding the right person at the right time for the job that fits the person according to skills and qualifications. This task needs a platform that supports making the right decision based on historical managerial information. The data mart is a departmental based decision support system that used departmental data to help decision-makers to support short term decisions. Human resource (HR) data mart is the base stone for building an enterprise data warehouse. The paper presents the implementation process of HR data mart starting from implementing data mart schema to online analytical processing (OLAP) reports. The data mart is implemented on retired employees' data of Basr...

Satellite data are used in several environmental applications, particularly in air quality supervising, climate change monitoring, and natural disaster predictions. However, remote sensing (RS) data occur in huge volume, in near-real time, and are stored inside complex structures. We aim to prove that satellite data are big data (BD). Accordingly, we propose a software as an extract-transform-load tool for satellite data preprocessing. We focused on the ingestion layer that will enable an efficient RSBD integration. As a result, the developed software layer receives data continuously and removes ∼86% of the unused files. This layer also eliminates nearly 20% of erroneous datasets. Thanks to the proposed approach, we successfully reduced storage space consumption, enhanced the RS data accuracy, and integrated preprocessed datasets into a Hadoop distributed file system.

Satellite data are used in several environmental applications, particularly in air quality supervising, climate change monitoring, and natural disaster predictions. However, remote sensing (RS) data occur in huge volume, in near-real time, and are stored inside complex structures. We aim to prove that satellite data are big data (BD). Accordingly, we propose a software as an extract-transform-load tool for satellite data preprocessing. We focused on the ingestion layer that will enable an efficient RSBD integration. As a result, the developed software layer receives data continuously and removes ∼86% of the unused files. This layer also eliminates nearly 20% of erroneous datasets. Thanks to the proposed approach, we successfully reduced storage space consumption, enhanced the RS data accuracy, and integrated preprocessed datasets into a Hadoop distributed file system.

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the priva...

Disease outbreak detection, monitoring and notification systems play an important role in assessing threats to public health since disease outbreaks are becoming increasingly common world-wide. There are several systems in use around the world, with coverage of national, international and global disease outbreaks. These systems use different taxonomies and classifications for the detection and prioritization of potential disease outbreaks. In this paper, we study and analyze the current disease outbreak systems. Subsequently, we extract features and functions of typical and generic disease outbreak systems. We then propose a generic model for disease outbreak notification systems. Our effort is directed towards standardizing the design process for typical disease outbreak systems.

Makalah ini bertujuan memberikan gambaran umum tentang definisi suatu Data Master dan Master Data Management (MDM) yang dapat diterapkan di perusahaan industri perbankan untuk mengoptimalkan proses binsis mereka, mulai dari tugas skala kecil , bisnis funding, bisnis lending, hingga tugas yang bersifat laporan kepada internal maupun regulator seperti Bank Indonesia (BI), OJK (Otoritas Jasa keuangan) , PPATK (Pusat Pelaporan dan Analisis Transaksi Keuangan). Pada makalah ini juga menyoroti tentang studi kasus yang telah dilakukan oleh Tapan kumar Das and Manas Ranjan Mishra pada Journal yang berjudul "A Study on Challenges and Opportunities in Master Data Management". Dimana kesimpulan dari penelitian tersebut bisa kita jadikan sebagai alternatif solusi penggunaan teknologi MDM yang dapat meyelesaikan kasus di industry perbankan.

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students' academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students' degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows t...

In recent years, there is a tremendous growth in data volume. Also many other sources than traditional structured data ex. log files, web data, stream data and sensor data need to be stored in the OLTP. It is not suitable for an organization to neglect valuable information from these sources. ETL tools extract meaningful information from various data sources, various transformations of data are carried out in transformation phase and then load into the data warehouse. Traditionally, commercial ETL (Extract –transform-load) tools ex. Informatica, Micro strategy ,Pentho etc. used to transfer OLTP data to other database known as data warehouse . MapReduce technology is becoming popular among people with
specialty of ETL task compelling organization to gain benefits from it. Hadoop, an open source MapReduce framework, is capable of handling massive data, provide cheap storage, process structured as well as unstructured data and has massive scalability. It can be seen as viable alternative for migrating ETL job. Although Hadoop is beneficial for large scale industries, many small organizations having small amount of data is also looking for leveraging their business intelligence on it. In this paper, we will explore the new opportunities of utilizing Hadoop for performing business intelligence with specifically ETL phase of datawarehouse.

In Data Warehouse (DW) scenarios, ETL (Extraction, Transformation, Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into the DW. In this paper, we present a framework for the design of the DW back-stage (and the respective ETL processes) based on the key observation that this task fundamentally involves dealing with the specificities of information at very low levels of granularity including transformation rules at the attribute level. Specifically, we present a disciplined framework for the modeling of the relationships between sources and targets in different levels of granularity (including coarse mappings at the database and table levels to detailed inter-attribute mappings at the attribute level). In order to accomplish this goal, we extend UML (Unified Modeling Language) to model attributes as first-class citizens. In our attempt to provide complementary views of the design artifacts in different levels of detail, our framework is based on a principled approach in the usage of UML packages, to allow zooming in and out the design of a scenario.

This article describes a Causal Analysis and Resolution of
a problem for the elaboration of a Data Mart – DM
specialized in Production Control and Planning – PCP.
The unified information in the proposed DM is loaded
form Management Information Systems – MIS, with
historic data from software development project. To allow
the integrated load from different data sources, it was
developed a Extract, Transform and Load – ETL process,
whose implementation has integrated data from the MIS
of Function Point Analysis and Personal Software
Process – PSP in a software factory. That allowed the
construction of a Decision Support System – DSS over the
constructed DM. This DSS allows the use of Online
Analytical Processing – OLAP to support quantitatively
the Process Areas of Measurement and Analysis – MA of
Capability Maturity Model Integration – CMMi

Recent educational research has underscored the criticality and centrality of teacher quality as a decisive and prominent influence on students' academic growth and success. Consequently, it becomes imperative to define, understand, and study the qualities and qualifications of effective teachers so as to inform preservice and in-service teacher education practices. Therefore, the primary objective of this study was to investigate administrators' views and expectations on teacher recruitment processes and qualities of English language teaching (ELT) professionals who work as instructors in intensive English programs at institutions of higher education in Turkey and Northern Cyprus. The study focused on 19 institutions of higher education in Turkey and Northern Cyprus and utilized a qualitative approach for data collection (semistructured face-to-face interviews, Skype interviews, and written responses) and analysis consistent with the principles of grounded theory methodology. The thematic analysis of the data revealed views on language teacher recruitment processes and practices in higher education and highlighted the vitality of language proficiency, openness for professional development and self-reflection, character, and pedagogical knowledge for ideal applicants for teaching positions. Administrators' perceptions regarding teacher recruitment processes and qualities of ELT professionals offer insights that are useful for developing contextually sensitive preservice and in-service teaching practices.