Data Warehousing Training
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Data Warehousing Training
Data Warehousing Training, Data Modeling Training, Erwin Training Course Data Warehousing Training course gives you complete insight about BI …
Data Warehousing Training, Data Modeling Training, Erwin Training Course
Data Warehousing Training course gives you complete insight about BI and it will enable you to learn about introduction Data modeling, Multidimensional Model, and Introduction to RDBMS & Database Vs Data warehouse, Dimensional Modeling, Cube, and Erwin.
why go for Data warehousing training
Data warehousing training lets people look at corporate data as never before. Patterns, trends, seeing the forest and the trees all became a possibility with data warehousing. Business people were able to look at their enterprises as no one had ever been able to previously and entire industries and multi-billion dollar companies grew out of this love of knowing more. Companies saw that business intelligence and data warehousing allowed them to make important corporate decisions based on new perspectives gained from data gathered from all over the corporation.
However, the thirst for more insight into data created by data warehousing training techniques created a virtual flood of data. Data Warehouses stored detailed data, historical data, and data integrated from a wide variety of sources.
Data Warehousing Training course is designed for beginners to advance level professionals.
Basic knowledge of sql
Business Intelligence Professionals or ETL Developers
Professionals aspire to become Business Object Professionals or Business Intelligence Professionals
Project Managers, Database Professionals, Mainframe Professionals, SQL Develope
A common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon:
- Subject Oriented
- Time Variant
Data warehouses are designed to help you analyze data. For example, to learn more about your company’s sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like “Who was our best customer for this item last year?” This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.
Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.
Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.
In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. A data warehouse’s focus on change over time is what is meant by the term time variant.
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At the end of the course there will be a quiz and project assignments once you complete them you will be awarded.