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Summarized Content

Hiya! I simply want to give a huge thumbs up for the great info you have got here on this post. I might be coming back to your blog for You have got a really informative blog here… thanks and keep writing… Thank you for your comment Suda. Yes I created this page manually. definitely be back for updates. I’m especially interested in using AMO with Powershell and your great examples of SSAS DMVs. I had never I have read your book Dataware house with SQL Server. It realy super!. Thanks for sharing the information For details DWH & BI concepts and DWH & sql interview related materials please visit on this link There is lot information available in this blog about BI and data warehousing. Would follow this blog regularly for any update on Oracle Always a pleasure to read your blog! Very useful information. Please continue your excellent blog many years… Privacy & Cookies: This site uses cookies. By continuing to use this website, you agree to their use.


Dwbi1 Main Page Content

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Title: Data Warehousing, BI and Data Science | Vincent Rainardi's blog on DW, BI, Big Data and Machine
Description: Data Warehousing One or Two Dimensions Using a Data Warehouse for CRM A dimension with only one attribute Data Warehouse Data Modelling (SQLBits 4 Persentation) Storing Percentage Measure in the Fact Table Primary Key in Accumulating Snapshot Fact Tables Join Brings More Rows Normalised Data
H1: Data Warehousing, BI and Data ScienceIs it informative enough?
H2: 14 Comments »Is it informative enough?
H3: Content of This BlogIs it informative enough?

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/comments/feed/:
Title

Comments for Data Warehousing, BI and Data Science

Description

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/2010/06/17/one-or-two-dimensions/:
Title

One or Two Dimensions | Data Warehousing, BI and Data Science

Description

Each customer can have more than one account. Should we a)  create one dimension: dim_account, with grain = account, PK = account_key, and customer details become attributes of this dimension. On the fact table we only have account_key FK. b)  create two dimensions: dim_account (grain = account, PK = account_key) and dim_customer (grain: customer, PK: customer_key). On…

H1

Data Warehousing, BI and Data Science

H2

17 June 2010

H3

One or Two Dimensions

/2010/01/28/using-data-warehouse-for-crm/:
Title

Using a Data Warehouse for CRM | Data Warehousing, BI and Data Science

Description

Usually a data warehouse is used for Business Intelligence. In this article we will describe another usage of a data warehouse: for Customer Relationship Management (CRM). Customer Relationship Management is a set of activities performed by a company or organization (business and non-business) to manage and yse information about their customers, to keep in contact…

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H1

Data Warehousing, BI and Data Science

H2

28 January 2010

H3

Using a Data Warehouse for CRM

/2010/03/11/a-dimension-with-only-one-attribute/:
Title

A dimension with only one attribute | Data Warehousing, BI and Data Science

Description

If a dimension only has one attribute, is it worth putting the attribute in the fact table instead? Why should we bother to create a separate table for that dimension, with 2 columns: dim key and that single attribute? That will make the query slow right? Consider a simple 1 dim 1 fact scenario: create…

H1

Data Warehousing, BI and Data Science

H2

11 March 2010

H3

A dimension with only one attribute

/2009/04/20/dimensional-modelling-sqlbits-presentation/:
Title

Data Warehouse Data Modelling (SQLBits 4 Presentation) | Data Warehousing, BI and Data Science

Description

My presentation at SQLBits 4, 28th March 2009 in Manchester, is here: Data Warehouse Data Modelling. Description: In this session we will discussed what is dimensional data model, why do we use it for data warehousing, when to use and when not to use, what are the advantages and disadvantages and what are the alternatives.…

H1

Data Warehousing, BI and Data Science

H2

20 April 2009

H3

Data Warehouse Data Modelling (SQLBits 4 Presentation)

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