Profiler Object:Introduction - Revision history

Admin at 21:54, 2 October 2015

2015-10-02T21:54:19Z

← Older revision		Revision as of 21:54, 2 October 2015
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	==Overview==		==Overview==
	Melissa ~~Data’s~~ Data Profiler is an object that can be used to analyze a table’s data within custom applications or directly within leading database management systems. The analysis provides a great number of statistics at varying levels of detail. Using these statistics, a user can make educated decisions on what strategies he may need to employ to handle the data.		Melissa Data’s® Data Profiler Object® is an object that can be used to analyze a table’s data within custom applications or directly within leading database management systems. The analysis provides a great number of statistics at varying levels of detail. Using these statistics, a user can make educated decisions on what strategies he may need to employ to handle the data.

	===Supported Data Profiling Techniques===		===Supported Data Profiling Techniques===

Admin at 00:11, 3 March 2015

2015-03-03T00:11:51Z

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	~~[[Profiler Object:Reference\|← Profiler Object Reference]]~~		{{ProfilerIntroductionNav}}

	{~~\| class="mw-collapsible" cellspacing="2" style="background-color:#f9f9f9; border:1px solid #aaaaaa; font-size:9pt; color:#0645ad; padding:8px 8px;"~~
	~~! style="text-align:left; color:black; border-bottom:1px solid #aaaaaa;"\|'''Profiler Object Navigation'''~~
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	~~\|[[Profiler Object:Introduction\|Introduction]]~~
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	~~\|[[Profiler Object:System Requirements\|System Requirements]]~~
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	~~\|[[Profiler Object:Licensing\|Licensing]]~~
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	~~\|[[Profiler Object:Order of Operations\|Order of Operations]]~~
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Admin at 23:05, 30 January 2015

2015-01-30T23:05:49Z

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	*Content analysis relies on reference data to determine if the input data contains information consistent with what is expected.		*Content analysis relies on reference data to determine if the input data contains information consistent with what is expected.
	*Field analysis determines if the input data is consistently fielded, using the data contained in the entire record to analyze the context of the data.		*Field analysis determines if the input data is consistently fielded, using the data contained in the entire record to analyze the context of the data.


	~~==Demo Mode==~~
	~~Profiler Object API has a Demo mode (no license is necessary) that will allow you to:~~

	*Process up to 50k records per run.
	*Result codes are all suppressed.
	*The AppendMode is forced to be Overwrite only.


	[[Category:Profiler Object]]		[[Category:Profiler Object]]

Admin at 18:50, 30 January 2015

2015-01-30T18:50:13Z

← Older revision		Revision as of 18:50, 30 January 2015
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	*Content analysis relies on reference data to determine if the input data contains information consistent with what is expected.		*Content analysis relies on reference data to determine if the input data contains information consistent with what is expected.
	*Field analysis determines if the input data is consistently fielded, using the data contained in the entire record to analyze the context of the data.		*Field analysis determines if the input data is consistently fielded, using the data contained in the entire record to analyze the context of the data.


			==Demo Mode==
			Profiler Object API has a Demo mode (no license is necessary) that will allow you to:

			*Process up to 50k records per run.
			*Result codes are all suppressed.
			*The AppendMode is forced to be Overwrite only.


	[[Category:Profiler Object]]		[[Category:Profiler Object]]

Admin at 01:38, 16 January 2015

2015-01-16T01:38:47Z

← Older revision		Revision as of 01:38, 16 January 2015
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	\|[[Profiler Object:Introduction\|Introduction]]		\|[[Profiler Object:Introduction\|Introduction]]
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			\|[[Profiler Object:System Requirements\|System Requirements]]
	\|-		\|-
	\|[[Profiler Object:Licensing\|Licensing]]		\|[[Profiler Object:Licensing\|Licensing]]
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	{{CustomTOC}}		{{CustomTOC}}

	Melissa Data’s Profiler ~~Object~~ can be used to analyze a table’s data. ~~This~~ analysis provides ~~numerous~~ statistics at varying levels of detail. Using these statistics, ~~you~~ can make educated decisions on ~~your~~ data ~~handling strategies~~.		==Overview==
			Melissa Data’s Data Profiler is an object that can be used to analyze a table’s data within custom applications or directly within leading database management systems. The analysis provides a great number of statistics at varying levels of detail. Using these statistics, a user can make educated decisions on what strategies he may need to employ to handle the data.

			===Supported Data Profiling Techniques===

			====Discovery====
			The analysis of new data before it is inserted into a Data Warehouse. This analysis is used to ensure that the data is correctly fielded, consistently formatted, standardized, etc. Because it can be very difficult to fix problems once data has been merged into a Data Warehouse, it is critical that issues are detected and eradicated prior to the merge.

			====Monitoring====
			The continual analysis of warehoused data in an effort to ensure a consistent quality of data. In systems where records are actively inserted, updated and deleted, it is nearly impossible to maintain a comprehensive set of business rules that foresee every situation. In addition, in systems that support multiple methods of access (ie, web, desktop, tablet/phone), it can be difficult to ensure that all program code adequately enforces all business rules.

	The Profiler Object ~~was~~ designed with ~~three profiling modes in mind: Discovery, Monitoring~~, and ~~Incremental Monitoring~~.		===Columns and Data Types===
			The Profiler Object is designed to work with a variety of column types, and analyzes data to ensure that it adheres to the limitations imposed by the user-specified type.

	~~===Discovery===~~		*Numeric:Integers (8, 16, 32 or 64-bit), Floats (single or double), Decimal and Currency.
	~~Discovery is the analysis of new data before it is inserted into a Data Warehouse. This analysis is used to ensure that the data is correctly fielded~~, ~~consistently formatted~~, ~~standardized~~, ~~etc~~. ~~Because it can be very difficult to fix problems once data has been merged into a Data Warehouse~~, ~~it is critical that issues are detected~~ and ~~eradicated prior to the merge~~.		*String:Unicode and Multi-byte, both fixed- or variable-length.
			*Date and/or Time, of varying resolutions.
			*Boolean

	===~~Monitoring~~===		===Data Analysis Summary===
	~~Monitoring is the continual~~ analysis ~~of warehoused data in an effort to ensure a consistent quality of data. In systems where records are actively inserted, updated, and deleted, it~~ is nearly impossible to maintain a comprehensive set of business rules that foresee every situation. In addition, in systems that support multiple methods of access (ie, web, desktop, tablet/phone), it can be difficult to ensure that all program code adequately enforces all business rules.		Deep data analysis is performed on several levels:

	~~===Incremental Monitoring===~~		*General Formatting analysis is used to determine if the input data ‘looks’ like what is expected.
	~~Incremental Monitoring~~ is the analysis ~~of a record's~~ data ~~before it~~ is ~~inserted into a database~~. ~~This~~ analysis ~~uses a Result Code system to report~~ record ~~anomalies and inconsistencies~~ to ~~a calling program so that corrective action can be performed before~~ the ~~record is inserted~~.		*Content analysis relies on reference data to determine if the input data contains information consistent with what is expected.
			*Field analysis determines if the input data is consistently fielded, using the data contained in the entire record to analyze the context of the data.


	[[Category:Profiler Object]]		[[Category:Profiler Object]]

Admin: Created page with "← Profiler Object Reference {| class="mw-collapsible" cellspacing="2" style="background-color:#f9f9f9; border:1px solid #aaaaaa; font-size:..."

2014-12-30T20:15:17Z

Created page with "← Profiler Object Reference {| class="mw-collapsible" cellspacing="2" style="background-color:#f9f9f9; border:1px solid #aaaaaa; font-size:..."

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[[Profiler Object:Reference|← Profiler Object Reference]]

{| class="mw-collapsible" cellspacing="2" style="background-color:#f9f9f9; border:1px solid #aaaaaa; font-size:9pt; color:#0645ad; padding:8px 8px;"
! style="text-align:left; color:black; border-bottom:1px solid #aaaaaa;"|'''Profiler Object Navigation'''
|-
| style="padding-right:220px;"|
|-
|[[Profiler Object:Introduction|Introduction]]
|-
|[[Profiler Object:Licensing|Licensing]]
|-
|[[Profiler Object:Order of Operations|Order of Operations]]
|}

{{CustomTOC}}

Melissa Data’s Profiler Object can be used to analyze a table’s data. This analysis provides numerous statistics at varying levels of detail. Using these statistics, you can make educated decisions on your data handling strategies.

The Profiler Object was designed with three profiling modes in mind: Discovery, Monitoring, and Incremental Monitoring.

===Discovery===
Discovery is the analysis of new data before it is inserted into a Data Warehouse. This analysis is used to ensure that the data is correctly fielded, consistently formatted, standardized, etc. Because it can be very difficult to fix problems once data has been merged into a Data Warehouse, it is critical that issues are detected and eradicated prior to the merge.

===Monitoring===
Monitoring is the continual analysis of warehoused data in an effort to ensure a consistent quality of data. In systems where records are actively inserted, updated, and deleted, it is nearly impossible to maintain a comprehensive set of business rules that foresee every situation. In addition, in systems that support multiple methods of access (ie, web, desktop, tablet/phone), it can be difficult to ensure that all program code adequately enforces all business rules.

===Incremental Monitoring===
Incremental Monitoring is the analysis of a record's data before it is inserted into a database. This analysis uses a Result Code system to report record anomalies and inconsistencies to a calling program so that corrective action can be performed before the record is inserted.

[[Category:Profiler Object]]