Extract, transform, load

In today 's organizations, basic descision making procedures and day by day operations frequently rely upon information that is put away in an assortment of information stockpiling frameworks, arrangements, and areas. To transform this information into helpful business data, the information commonly should be consolidated, purified, institutionalized, and compressed. For example, data may be changed over to an alternate information sort or different database servers may store the vital information

Kudler Dimensional Model Hands-On-Project Erwin Martinez DBM-460 March 14, 2011 Daniel McDonald Kudler Dimensional Model Hands-On-Project Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information

applying business rules; loading the data into a data warehouse or any other database or application that houses data. ETL Tools provide facility to extract data from different non-coherent systems, transform (cleanse & merge it) and load into target systems. The main goal of maintaining an ETL process in an organization is to migrate and transform data from the source OLTP systems to feed a data warehouse and form data marts. ETL process is the basis of BI and it is a prime decisive factor for success

Preface Purpose The purpose of this document is to detail the description of the Real Time (Active) Fraud Detection in Banking Transactions (FDBT) Project. This document is a key project artifact and is used during the design, construction, and rollout phases. Scope The objective of this project report is to capture the functional and non-functional requirements for the Real Time FDBT project. This report lists out the complete system requirements and design architecture of the project

Case Study: A Data Warehouse for an Academic Medical Center Jonathan S. Einbinder, MD, MPH; Kenneth W. Scully, MS; Robert D. Pates, PhD; Jane R. Schubart, MBA, MS; Robert E. Reynolds, MD, DrPH ABSTRACT The clinical data repository (CDR) is a frequently updated relational data warehouse that provides users with direct access to detailed, flexible, and rapid retrospective views of clinical, administrative, and financial patient data for the University of Virginia Health System. This article presents

for Extract, Transform, and Load. While it sounds like a very

for a data warehouse are: 1. Source Systems 2. Data Staging Area 3. Presentation servers The data travels from source systems to presentation servers via the data staging area. The entire process is popularly known as ETL (extract, transform, and load) or ETT (extract, transform, and transfer). Oracle’s ETL tool is called Oracle Warehouse Builder (OWB) and MS SQL Server’s ETL tool is called Data Transformation Services (DTS). A typical architecture of a data warehouse is shown below:

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store data into the EDW, it requires three steps of extract, transform and load. During the extraction process, the data is taken from the source of different systems within the USPS facilities. Then the transform process structures the data using rules or tables and turns it into one consolidated warehouse format. It also combines some data with others so it is easier to be transferred between different databases. The final process is the load with is basically integrating and writing the data into