KB483430: How to name metadata when viewing metadata IDs from multiple environments in a compliance telemetry dossier.

Senior Lead, Technical Support - Toronto • Strategy

The compliance telemetry dossier is Platform Analytics' built-in dossier that offers an additional solution for auditing license compliance telemetry. Additionally, Platform Analytics can analyze compliance results across multiple environments (i.e. metadata).

Starting with the release of Strategy ONE (March 2024), dossiers are also known as dashboards.

Symptom

During a compliance check, there is no built-in distinction of the different metadata IDs when using multiple sources of data from different environments.

Expected Behavior

Since you are monitoring metadata across multiple environments, there should be the ability to rename metadata ID so that it is easy to tell which environment is from. Naming conventions should be something like Dev/QAS/UAT/Prod.

Workaround

To differentiate between metadata IDs, you can create an Excel file with two columns, one for the ID and one for the description.
For example,

iserver.requestTimeout
The XML call's socket default read timeoutConfigDefaults.propertiesdocument.polling.timeoutThe maximum waiting time when polling a job on a server. This only works for a document or dossier instance. When polling the result, you may send multiple requests to the server. This value is the total time of all of these requests. Each single request timeout value is set by iserver.requestTimeout.ConfigDefaults.propertiesisAliveRequestTimeoutMillisThe socket read request timeout value when the Auth Filter sends a GetFolderID XML API request to an Intelligence server nodesys_defaults.xmlisAliveCheckIntervalMillisThe time interval of the check when the Auth Filter sends a GetFolderID XML API request to an Intelligence server nodesys_defaults.xmlBefore fixAfter fix


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID

SAP HANA DatatypeStrategy DatatypeAlphanum(n)VarChar(n)BigInt Big IntegerBinaryBinary(1)BooleanBinary(1)Char(n)Char(n)Char Varying(n)VarChar(n)DateDateDayDateDateDecimal(p,s)Decimal(p,s)DoubleDoubleFloat(n)If n>24 Double
If n<=24 Real IntegerInteger(10)LongDateTimeStampNchar(n)NChar (n)Numeric(p,s)Decimal(p,s)NVarChar(n)NVarChar(n)RealRealSecondDateTimeStampShortText(n)VarChar(n)SmallIntInteger(5)TextLongVarChar(n)TimeTimeTimeStampTimeStampTinyIntInteger(3)VarBinary(n)VarBin(n)VarChar(n)VarChar(n)Order NumberDate ShippedDate Received11/1/191/5/1921/1/191/5/1931/1/191/14/19Actual ResultExpected Result[Tot Cost]/1000Actual ResultExpected Resultmetadata_idmetadata_name6533805907426414592 Dev6533805907426417424 QAS


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID

After, import the data into a cube with a Metadata attribute (mapped to project schema) and a Metadata Name attribute (local attribute), and define the relationship between Metadata and Metadata Name = 1:1.
Use this cube in your compliance telemetry dossier, then filter by Metadata Name.
For more information on monitoring multiple environments, see Monitor Metadata Repositories Across Multiple Environments.

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Details

Knowledge Article

Published:

June 28, 2019

Last Updated:

March 21, 2024

Symptom

During a compliance check, there is no built-in distinction of the different metadata IDs when using multiple sources of data from different environments.

Expected Behavior

Workaround

To differentiate between metadata IDs, you can create an Excel file with two columns, one for the ID and one for the description.
For example,


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID


Tables Accessed:
STATE_SUBCATEG_MNTH_SLS          
LU_MONTH    

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from STATE_SUBCATEG_MNTH_SLS a11      
join LU_MONTH a12  
on (a11.MONTH_ID = a12.MONTH_ID)
group by a12.YEAR_ID


Tables Accessed:
LU_QUARTER
QTR_CATEGORY_SLS 

SQL Statements:
...
select a12.YEAR_ID  YEAR_ID,
sum(a11.TOT_COST)  WJXBFS1
from QTR_CATEGORY_SLS a11
join LU_QUARTER a12         
on (a11.QUARTER_ID = a12.QUARTER_ID)
group by a12.YEAR_ID