Find Source system
Find target system
Transform data
Load
Validate the data
There are some great reference sites for finding information about data warehousing concepts. Some sites that offer information are "Learn Data Modeling", "DW Info Center" and the Oracle website.
The concept of a staging table is not specific to Informatica, but is a common term used in datawarehousing and ETL. It is a database table used to store data prior to loading it into the main data warehouse tables. The name reflects that it a stage on the data's journey from source to target (rather like a staging post for a stagecoach). Typically this data would have been extracted from a source system database, and the staging table would have a structure similar to the source table. The database schema used to hold these staging tables is referred to as the staging area or the relational staging area. Data might also be stored in files prior loading in the warehouse - such files are called stage files and the directories in whch they are kept are known as the file staging area.
Data warehousing is adopting modern approaches such as cloud-based solutions, big data technologies, and machine learning for advanced analytics. Organizations are also shifting towards a more agile and scalable data architecture to handle the growing volumes of data. Moreover, there is an increasing focus on real-time data processing and integration to support faster decision-making.
Data warehousing and data mining contribute to Management Information Systems (MIS) by providing a centralized location for storing and accessing data, enabling users to run complex queries and generate reports for strategic decision-making. Data mining techniques help uncover patterns and trends in the data, allowing organizations to gain valuable insights and make informed decisions based on the information retrieved from the data warehouse. Ultimately, these tools enhance the effectiveness of MIS by facilitating more efficient data analysis and interpretation.
1. Data Marts design(Modelling) -- contains dimension and fact tables 2. ETL (extract transform load) -- how to load data from multiple db to datamart with transformation logic. e.g. using Informatica, Microsoft SSIS, Data Stage 3. Business Intelligence - Reporting e.g. BO,Cognos etc
Flint solutions is one of the major institutes in Bangalore to teach data warehousing. Big data also now competes with Data warehousing.
Because businesses wanted to integrate their data, data warehousing was born. Dating back to the late 1980s, data warehousing is simply a single system that stores all of a company's data.
what role should HIM professionals play in data warehousing development
Data Warehousing
Data warehousing software is used to catalog and record data for analysis and reporting. You can learn more about data warehousing from the Wikipedia. Once on the page, type "Data warehouse" into the search field at the top of the page and press enter to bring up the information.
Interested in learning about Data Warehousing? Attend a virtual seminar on Data Warehousing given by our AI bot, Tom. http://www.keysoft.co.in/virtualcourse.aspx (note: you will need audio output)
Data Warehousing is the process of unifying data from multiple data sources under a single unified schema. A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.
In computing, a data warehouse (DW) is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting.A data warehouse maintains its functions in three layers: staging, integration, and access. Staging is used to store raw data for use by developers. The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users.This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
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There are some great reference sites for finding information about data warehousing concepts. Some sites that offer information are "Learn Data Modeling", "DW Info Center" and the Oracle website.
There are many companies that offer Business Intelligence and Data Warehousing services. Some examples of companies who offer Business Intelligence and Data Warehousing services includes Plasma Comp and iDashboards.
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