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Some considerations for Infrastructure in Data Warehouse

Data Warehouse infrastructure estimation is complex, as it is difficult to judge the use the Data Warehouse might be put to. Here are some considerations, which can help you to better estimate.
 
This page of 'Principles and Rules' is linked to:  Data Warehousing, Data Analysis/OLAP, BI platform Tools Evaluation,

Here are some tips on managing infrastructure and licensing in a data warehouse (you can refer Data Warehouse infrastructure for a context).

Disk-Size Estimate for Data Warehouse

Do not over invest into your hard disk space. Be liberal in your estimates, but don't overdo. The reasons are:

  • The Hard disk space cost is falling by the day.
  • The Hardware vendors are providing the platforms, where you can be fairly incremental in terms of storage space that you can add.
  • The Data Warehouse platforms are extensible in terms of modular addition of hard-disk space.

Therefore, while you should not be adding hard-disk every 6 months, but one should not invest for next 3 years now.

Plan your disc compression for Data Warehouse:

Typically disc compression is done at the moment of crises, when you are running out of the disk space. The disk compression should be planned ahead in time, so that you get enough time to go for planning and acquiring additional infrastructure (which may take 4-5 months). The compression should be used pro-actively so that you always have 20-25% spare capacity.

Plan it in context of the Data Warehouse end-user tools

A data warehouse house is a single point repository for the organization data. Many more layers sit on top of it. For example OLAP server, Enterprise Reporting, Analytics tools etc...

Number of end-users

This end-user tool may reduce significantly the number of users which actually log into the data warehouse. For example an enterprise reporting system** can access the data warehouse in form of few users to generate all the enterprise reports Post that, the actual users are accessing the database and reports repository of the enterprise reporting system and not that of the data warehouse. Similarly, you might be using an analytics system, which creates its own local cube from a data warehouse. The actual users may be accessing that cube without logging into the data warehouse.

Number of viewer and designer licenses:

The end-user tools will have their own viewer and designer licenses, which may significantly reduce the end-user licenses for the data warehouse.

Processing Speed:

It is possible that data mining and analytics tools will work with the OLAP server for most of the processing. This means that once OLAP server is populated with the Data Warehouse data, the number of applications directly using the DW will reduce. Most of the applications with the OLAP layer will use DW for enterprise reporting, ad-hoc detail queries, or when one needs to drill down to transaction levels.


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Relevant Links to this page
Principles & Rules → Dimensional model has to be aligned to the Entity-Relationship → Principles & Rules → Always Use Conformed Dimensions → Principles & Rules → You may not be a able to have a perfect ETL → Practice Techniques → Handling Sparse Dimensional tables → Principles & Rules → Do not separate the parent and child line item data → Practice Techniques → Managing time-stamps across multiple time-zones → Practice Techniques → Recording events in multiple currencies → Practice Techniques → Handle different units of measure in the same fact table → Principles & Rules → Handling of Null foreign Keys in fact tables → Principles & Rules → Dimension Attributes as NULL → Principles & Rules → Don't rely too much on Meta Data Tools to enforce Business Intelligence → Principles & Rules → Don't wait for universal models for Data Marting → 
 
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Relevant links to this page
Dimensional model has to be aligned to the Entity-Relationship
Always Use Conformed Dimensions
You may not be a able to have a perfect ETL
Handling Sparse Dimensional tables
Do not separate the parent and child line item data
Managing time-stamps across multiple time-zones
Recording events in multiple currencies
Handle different units of measure in the same fact table
Handling of Null foreign Keys in fact tables
Dimension Attributes as NULL
Don't rely too much on Meta Data Tools to enforce Business Intelligence
Don't wait for universal models for Data Marting
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