< Prev		CONTENTS		Source		Next >

ARCHITECTURE AND PROCESSES IN A DATA WAREHOUSE

The architecture and processes in an enterprise data warehouse (EDW) will typically look as illustrated in Exhibit 5.1. The exhibit is the pivot for the rest of this chapter.

As opposed to the approach we've used so far in this book, we will now discuss the data warehouse based on the direction in which data and information actually move (from the bottom up). Our point of departure in previous chapters has been the direction that is dictated by the requirements for information (from the top-down). The bottom-up approach here is chosen for pedagogical reasons and reflects the processes that take place in a data warehouse. This does not, however, change the fact that the purpose of a data warehouse is to collect information required by the organization's business side.

As is shown by the arrows in Exhibit 5.1, the extract, transform, and load (ETL) processes create dynamics and transformation in a data warehouse. We must be able to extract source data into the data warehouse, transform it, merge it, and load it to different locations. These ETL processes are created by an ETL developer.

ETL is a data warehouse process that always includes these actions:

? Extract data from a source table.
? Transform data for business use.
? Load to target table in the data warehouse or different locations outside the data warehouse.

The first part of the ETL process is an extraction from a source table, staging table, or from a table within the actual data warehouse. A series

Exhibit 5.1 Architecture and Processes in a Data Warehouse of business rules or functions are used on the extracted data in the transformation phase. In other words, it may be necessary to use one or more of the transformation types in the following section.

Source

< Prev		CONTENTS		Source		Next >

Parallel Processing of Microarray Data
High throughput experimental platforms, such as mass spectrometry, microarray, and next generation sequencing, are producing an overwhelming amount of data yielding to the so called omics world. Among the many omics disciplines, genomics, proteomics, and interactomics and are gaining an increasing interest...
(Bioinformatics and Biomedical Engineering)
Parallel Processing of Microarray Data
High throughput experimental platforms, such as mass spectrometry, microarray, and next generation sequencing, are producing an overwhelming amount of data yielding to the so called omics world. Among the many omics disciplines, genomics, proteomics, and interactomics and are gaining an increasing interest...
(Bioinformatics and Biomedical Engineering)
General Framework for Processing Graph Structured Data
In this section, we will provide a general framework for modelling graphs [3]. Consider a node n in the graph. This node will be linked to other nodes through links. Assume for simplicity sake that the graph contains only un-directed links It will be slightly more complex in terms of...
(Handbook on Neural Information Processing)
Collection and Processing of the Data
For our analysis we needed two types of data. First, the patent data of the inventions in the fields of e-mobility and fuel cells made in the region of Stuttgart. Second, citations to these patents. Accordingly, two datasets were prepared. Each consisted of two parts: the information about e-mobility...
(Innovation Networks for Regional Development: Concepts, Case Studies, and Agent-Based Models)
Data Warehouse
Database specialists and ETL developers receive requirements from the analytical environment about data deliveries. If the required data is already in the warehouse, the process will be to make this data accessible to the front-end applications of the business. If data is not stored, the data warehouse...
(Business analytics for managers: taking business intelligence beyond reporting)
Data Warehouse
The analyst needs the data warehouse to provide him with data on the listeners' ages, genders, and tastes and preferences 24/7. He needs this information for the profiles. The database specialist does not have this data stored, and it cannot be obtained from an external supplier. Therefore, the database...
(Business analytics for managers: taking business intelligence beyond reporting)
Business Analytics at the Data Warehouse Level
During the last couple of years, a lot of changes have happened at the data warehouse level, and we can expect many more changes in the future. One of the major changes was called by the phrase Big Data. The reports that created this term came from McKinsey Global Institute in June 2011. The report...
(Business analytics for managers: taking business intelligence beyond reporting)
WHY A DATA WAREHOUSE?
The point of having a data warehouse is to give the organization a common information platform, which ensures consistent, integrated, and valid data across source systems and business areas. This is essential if a company wants to obtain the most complete picture possible of its customers. To gather...
(Business analytics for managers: taking business intelligence beyond reporting)
The Data Warehouse: Functions, Components, and Examples
In the actual data warehouse, the processed and merged figures from the source systems are presented (e.g., transactions, inventory, and master data). A modern data warehouse typically works as a storage area for the organization's dimensions as well as a metadata repository. First, we'll look at the...
(Business analytics for managers: taking business intelligence beyond reporting)