ARRANGEMENT AND MODULATION OF ETL PROCESS IN THE STORAGE

Data warehouse (DW) is the basis of systems for operational data analysis (OLAP-Online Analytical Processing). Data extracted from different sources transforms and load in DW. Proper organization of this process, which is called ETL (Extract, Transform, Load) has important significance in creation of DW and analytical data processing. Forms of organization, methods of realization and modeling of ETL processes are considered in this paper.Data warehouse (DW) is the basis of systems for operational data analysis (OLAP-Online Analytical Processing). Data extracted from different sources transforms and load in DW. Proper organization of this process, which is called ETL (Extract, Transform, Load) has important significance in creation of DW and analytical data processing. Forms of organization, methods of realization and modeling of ETL processes are considered in this paper

A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data

Many interesting data sets available on the Internet are of a medium size---too big to fit into a personal computer's memory, but not so large that they won't fit comfortably on its hard disk. In the coming years, data sets of this magnitude will inform vital research in a wide array of application domains. However, due to a variety of constraints they are cumbersome to ingest, wrangle, analyze, and share in a reproducible fashion. These obstructions hamper thorough peer-review and thus disrupt the forward progress of science. We propose a predictable and pipeable framework for R (the state-of-the-art statistical computing environment) that leverages SQL (the venerable database architecture and query language) to make reproducible research on medium data a painless reality.Comment: 30 pages, plus supplementary material

Scaling data streams with Catmandu and linked data fragments

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Data Warehouse And Data Mining – Neccessity Or Useless Investment

The organization has optimized databases which are used in current operations and also used as a part of decision support. What is the next step? Data Warehouses and Data Mining are indispensable and inseparable parts for modern organization. Organizations will create data warehouses in order for them to be used by business executives to take important decisions. And as data volume is very large, and a simple filtration of data is not enough in taking decisions, Data Mining techniques will be called on. What must an organization do to implement a Data Warehouse and a Data Mining? Is this investment profitable (especially in the conditions of economic crisis)? In the followings we will try to answer these questions.database, data warehouse, data mining, decision, implementing, investment

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201

Analisis ETL (Extract, Transform, Load) pada Real Time Data Warehouse ETL (Extract, Transform, Load) Analysis in Real Time Data Warehouse

ABSTRAKSI: Data Warehouse semakin banyak diterapkan oleh banyak perusahaan besar. Hal tersebut dipacu dengan semakin berkembangnya kebutuhan suatu perusahaan dalam mendapatkan informasi yang berkaitan dengan proses bisnisnya. Data Warehouse yang berisi rekaman data yang dimiliki perusahaan, di era E-Bussiness sekarang ini haruslah selalu menyajikan data yang up-to-date. Pada umumnya proses Data Warehouse akan direfresh per satuan waktu (per 2 hari, per 24 jam atau bahkan per jam). Namun akankah masih efektif cara tersebut dengan melihat teknologi informasi yang semakin berkembang, dimana perubahan data dapat terjadi kapanpun dan dimanapun, bahkan dalam hitungan detik data transaksi dapat mengalami perubahan hingga puluhan kali atau lebih. Oleh karena itu, untuk menjaga kevalidan data yang ada pada Data Warehouse maka diterapkanlah Real Time Data Warehouse.Real Time Data Warehouse memiliki perbedaan yang cukup signifikan pada proses ETLnya. Dimana proses ETL dilakukan tiap kali ada perubahan data pada sumber datanya. Sehingga keakuratan data yang tersimpan dalam warehouse lebih terjamin dan proses ETL tidak memakan waktu yang lama karena tidak semua data sumber dilibatkan, hanya yang mengalami perubahan saja pada waktu tertentu.Setelah proses implementasi dan pengujian Real Time Data Warehouse dilakukan, maka dapat dianalisa faktor-faktor yang menjadi kelebihan dan keunggulan sistem ini dibandingkan konvensional Data Warehouse. Analisa diutamakan dari segi keakuratan data, bentuk proses ETL dan penyajian informasi yang up-to-date.Kata Kunci : Real Time Data Warehouse, ETL, keakuratan data.ABSTRACT: Many big enterprise use Data Warehouse as their storage. It happens because every enterprise needs to get business process’s information. Nowadays, Data Warehouse which contains historical data of enterprise should provide up-to-date data. Generally, Data Warehouse’s process will be refresh periodically (twice a day, every 24 hours or every hours.). But it will not too effective, because technology grows faster, data can be change everytime even in seconds. Because of that, to prevent validation of data on Data Warehouse we can use real-time datawarehouse.Real–time datawarehouse has significant differences on ETL process where ETL process will be done when change of data happen on data source so the data which is stored in warehouse will be guaranteed. ETL process doen’t take a long time to do because not all data involved.After implementation and testing of real-time Data Warehouse done, we can analyze every factors that make real-time datawarehousemore excellent than konvesional Data Warehouse. Analysis of real-time Data Warehouse will be more spesific in data accuracy, ETL process dan providing up-to date informationKeyword: Real Time Data Warehouse, ETL, data accurac

Automatic generation of data merging program codes.

Data merging is an essential part of ETL (Extract-Transform-Load) processes to build a data warehouse system. To avoid rewheeling merging techniques, we propose a Data Merging Meta-model (DMM) and its transformation into executable program codes in the manner of model driven engineering. DMM allows defining relationships of different model entities and their merging types in conceptual level. Our formalized transformation described using ATL (ATLAS Transformation Language) enables automatic generation of PL/SQL packages to execute data merging in commercial ETL tools. With this approach data warehouse engineers can be relieved from the burden of repetitive complex script coding and the pain of maintaining consistency of design and implementation

Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS