Search for	Example	Results
A single word	`cat`	Topics that contain the word "cat". You will also find its grammatical variations, such as "cats".
A phrase. You can specify that the search results contain a specific phrase.	`"cat food"` (quotation marks)	Topics that contain the literal phrase "cat food" and all its grammatical variations. Without the quotation marks, the query is equivalent to specifying an OR operator, which finds topics with one of the individual words instead of the phrase.

Search for	Operator	Example
Two or more words in the same topic	`AND` `and` `+` (plus symbol) `&` (ampersand)	`cat AND dog` `"cat food"+milk` `"cat food"&"dog food"`
Either word in a topic	`OR` `or` `\|` (pipe)	`cat OR dog` `cat \| dog`
Topics that do not contain a specific word or phrase	`NOT` `not` `!` (exclamation point)	`NOT cat` `! dog`
Topics that contain one string and do not contain another	`^` (caret)	`cat ^ mouse`
A combination of search types	`( )` parentheses	`cat + (dog \| mouse)` `cat \| dog + (! mouse)`

IT Business Analytics10.10

Design and Analysis - Discover Data Evidence

It is necessary to understand and be familiar with the new data you want to develop. When there is a new source system involved, you must first understand how the data from the source appears and operates.

Discover data evidence

Data Evidence can be discovered as follows:

Understand how to extract the data.
Take sample files extracted from the source.
Examine the different fields that this source provides: It is important to recognize the fields and data that come from the source and to define the names of the fields as the source tables.
Define and plan in advance which extractors provide data for specific entities.
Understand variety, different lists of values, and end-cases.
Observe data behavior over time:
1. Understand what happens with data across time – is it the same file, only different dates?
2. Sizes of rows and volumes by types of customers.
3. Identify how this data correlates with existing data in the Data Warehouse.
  
  At the end you should have a good sense of what this data looks like and how to develop extraction code for it.
Identify:
1. What is the required data to be available in the Target schema to support required KPIs.
2. What is the data that can come from the source (depending on technology).
3. Where do they meet in the middle, so eventually we have the outcomes of the extracted data and the DWH Target model.
4. For which columns do you want to preserve history.

After mapping between the Source and the Target, the design process is complete.

Send Help Center feedback