"Live, Learn, Apply & Educate!": February 2012

Friday, February 24, 2012

Microsoft - Windows, Office - Daily Keyboard Shortcuts

Would you rather use keyboard shortcuts to increase your productivity?

Below find some of the commonly used keyboard shortcuts that I can say increases productivity. From my point of view knowing these shortcuts are worth not using the mouse.

Please share your daily shortcuts...

#	Key	Windows Functions
1	Alt + F4	Close Window, App, Shutdown
2	Alt + Tab	Switch Open Windows between Applications
3	Ctrl + Shift + Esc	Windows Task Manager
4	F2	Edit, Rename
5	F3	Search
6	F5	Refresh
7	Windows + D	Show Desktop
8	Windows + E	Open: My Computer
9	Windows + L	Lock screen
10	Windows + R	Open: Run command

#	Key	Office Functions
1	Ctril + I	Italize Text
23	Ctril + Z	Undo
2	Ctrl + A	Select All
3	Ctrl + Arrow(s)	Skip between words
4	Ctrl + B	Bold Text
5	Ctrl + C	Copy Selected Text
6	Ctrl + Enter	Page Break (Page Always Start in a New Page)
7	Ctrl + F	Find
8	Ctrl + F2	Print Preview
9	Ctrl + H	Find/Replace
10	Ctrl + O	Open File
11	Ctrl + P	Print
12	Ctrl + S	Save
13	Ctrl + Shift + Arrow	Select Whole Word
14	Ctrl + Shift + P	Change Text Size, Font, Style
15	Ctrl + U	Underline Text
16	Ctrl + V	Paste Copied/Cut Text
17	Ctrl + W	Close Window
18	Ctrl + X	Cut Selected Text
24	Ctrl + Y	Redo
19	F12	Save As
20	F2	Edit, Rename
21	F6	Point cursor at Address Bar
22	F7	Spell Check

Tuesday, February 14, 2012

Data Warehousing Concept Comparison Overview

Kimball vs. Inmon vs. Hybrid vs. Vault

Ralph Kimball - bottom-up design: approach data marts are first created to provide reporting and analytical capabilities for specific business processes.

Data marts - contain, primarily, dimensions and facts.

Facts - either contain atomic (detailed) data, and, if necessary, summarized data.
Single data mart often models a specific business area (unit) i.e. "Sales," "Production."

Integration of data marts is managed through implementation is called: "data warehouse bus architecture."
Data Warehouse bus architecture: is an implementation of "the bus," a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.

Conformed dimensions - is a set of data attributes that have been physically implemented in multiple database tables using the same structure, attributes, domain values, definitions and concepts in each implementation.
Integration Points(between data marts) - integration of the data marts in the data warehouse is centered on the conformed dimensions (residing in "the bus").
Drill across process - integration of two or more data marts process.

Works by grouping (summarizing) the data long the keys of the (shared) conformed dimensions of each fact participating in the "drill across" followed by a join on the keys of these grouped (summarized) facts.

Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse.
Important management task is making sure dimensions among data marts are consistent or "conform."

Advantage: data warehouse ends up being "segmented." into number of logically self contained (up and including The Bus) and consistent data marts, rather than a big and often complex centralized model.

Business value can be returned as quickly as the first data marts can be created.
i.e. data warehouse can start from "Sales" department, by building a Sales-data mart. And if business is expanded into "Production" department, then Production-data mart can be integrable, because they share the same "BUS."
If integration via the bus is achieved, the data warehouse, through its two data marts, will only be able to deliver the specific information that the individual data marts are designed to do, but integrated "Sales-Production" information, which, often is of critical business value.

Bill Inmon - top-down design: 1st author on the subject of data warehouse, as a centralized repository for the entire enterprise. Data warehouse design using normalized enterprise data model.

Inmon's vision the data warehouse is at the center of the "Corporate Information Factory (CIF)," which provides a logical framework for delivering business intelligence (BI) and business management capabilities.
Atomic data, data at the lowest level of detail, are stored in the data warehouse.
Dimensional data marts: are created containing data needed for specific business processes or department from the data warehouse.
Data warehouse is:

Subject oriented: the data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.
Non-volitile: data in the data warehouse are never over-written or deleted - once committed, the data are static (do not change), read-only, and retained for future reporting.
Integrated: data warehouse contains data from most or all of an organization's operational systems and these data are made consistent.
Time variant: top-down design methodology generates highly consistent dimensional views of data across data marts, since all data marts are loaded from the centralized repository.

Top-down: design is robust against business changes.
Generating new dimensional data marts against the data stores in the data warehouse is a relatively simple task.

Disadvantage: top-down design represents a very large project with a very broad scope.

Upfront cost for implementing a data warehouse is significant, and the duration of time from the start of project to he point that end users experience initial benefits can be substantial.
Inflexible and unresponsive to changing departmental needs during the implementation phases.

Hybrid design: data warehouse solutions often resemble hub and spoke architecture. Legacy systems feeding the DW/BI solution often include CRM and ERP, generating large amounts of data. To consolidate these various data models, and facilitate the ETL process, DW solutions often make use of an operational data store (ODS). The information then parsed into the actual DW.

Operational Data Store: is database designed to integrate data from multiple sources for additional operations on the data)
To reduce redundancy, large systems will often store data in a normalized way. Data marts for specific reports can then be built on top of the DW solution.
Important to note that DW database in a hybrid solution is kept on 3d normal form to eliminate data redundancy. Although normal relational database is not efficient for BI reports.
DW effectively provides a single source of information from which the data marts can read, creating a highly flexible solutions from a BI point of view.
Hybrid architecture: allows DW to be replaced with a master data management solution where operational, not static information could reside.

Master data management: comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization (which may include reference data).

Data Vault Modeling: is a hybrid design, consisting of the best of breed practices from both 3rd normal form and star-schema.

Data Vault model is not a true 3rd normal form, and breaks some of the rules that 3NF dictates be followed.
It is a top-down architecture with bottom-up design, geared to be strictly a data warehouse.
It is not geared to be end-user accessible, which when built, still requires the user of a data mart or star-schema based release are for business purposes.

Monday, February 13, 2012

In-Class vs. Home Work vs. Exam

Don't mind other people's business

One sunny day after Halloween, a little boy was enjoying a bag full of candies. As one of the neighbors was sitting out on the porch, noticed that the boy ate too much candy in a short period of time. So the neighbor approached the boy and said:

Neighbor: Hey sonny! You know eating too much candy is not good for you.
Boy: (After finishing another candy) You know (chewing) my grandpa lived till 90 (continued chewing)
Neighbor: What!? Eating candy???
Boy: No! Not minding other people's business!!!!