Compare Active Directory Environments Users/Groups/Group_Memberships

Question

I have 2 active directory environments we are doing a refresh on (development ("dev-ad.com") and pulling the data from production ("prod-ad.com")

I have connected and pulled each environment into its own report. The current environment we are refreshing is developement. I need to be able to ensure all Users, Groups and Group Memberships are as close to production as possible. Each environment is using a different domain so OU, DN, and CN will all have slights diffrent values due to this.

I am faily new to PowerBI, self-taught. So Im trying to figure out the best method for creating a report that will compare Dev AD with Prod AD.

Data:
dev_group
dev_group_memebers
dev_user
prod_group
prod_group_members
prod_user

There are too many columns to specify for each data group. Essentially I have;
- all attributes for users
- basic for groups
- group members i have group distinguishedName, member.displayName, member.employeeNumber, member.employeeType, member.userPrincipalName, group top.name.

I really could use alot of help here. I have been beeing my head against the wall trying to learn as much as I can in order to do all of this, which seems kind of advance but thats just my opinion.

anonymous · Answer

Hi&nbsp;Anonymous&nbsp;,
Please try below steps:
Steps1: Data PreparationGiven the complexity and volume of the data you're dealing with, the first step is to ensure your data is well-prepared for comparison. This involves structuring your data in a way that facilitates easy comparison despite the differences in domain names.&nbsp;

Normalize Domain-Specific Attributes: Since OU, DN, and CN have slight differences due to different domains, create calculated columns in Power BI to normalize these attributes. For example, you can create a new column that extracts the last part of the DN that is common across both environments.
Consolidate Data: Ensure that your data tables (dev_group, dev_group_members, dev_user, prod_group, prod_group_members, prod_user) are loaded into Power BI. You may need to transform these tables to have a consistent structure for comparison. Use Power Query Editor in Power BI for this purpose.

&nbsp;
Steps2: Creating RelationshipsEstablish relationships between your tables. Since you're dealing with users and groups across two environments, you'll likely need to create relationships based on normalized attributes that are common between development and production.
&nbsp;
Steps3: Comparison LogicTo compare the environments:

Create Measures for Comparison: Use DAX to create measures that identify discrepancies between the two environments. For example, you can create a measure that counts the number of users in a group in the production environment and compares it with the count in the development environment.
Visualize Differences: Use conditional formatting and visuals like tables and bar charts to highlight differences between the two environments. This will make it easier to identify where discrepancies lie.

&nbsp;
Best regards,Community Support Team_Binbin YuIf this post&nbsp;helps, then please consider&nbsp;Accept it as the solution&nbsp;to help the other members find it more quickly.

anonymous · Answer

Thank you for your response. I have done both steps 1 and 2 thus far and I am stuck on step 3. I am definately not that skilled wtih DAX and having to research each logical comparison with little luck. If there is any assistantace you can help with this, I would greately appreacite it.

anonymous · Answer

Hi&nbsp;Anonymous&nbsp;,
In order to better understand your demands and give the right solution, could you please provide some more specific information? such as your desensitized example data and a screenshot of your desired results?
&nbsp;
Thanks for your efforts &amp; time in advance.
&nbsp;
Best regards,Community Support Team_Binbin YuIf this post&nbsp;helps, then please consider&nbsp;Accept it as the solution&nbsp;to help the other members find it more quickly.

anonymous · Answer

So here is an overall view of the data layout.&nbsp;Top side we have Group TablesBottom side we have User TablesLeft Side is Development EnvironmentRight Side is Production EnvironmentHere is a list of the tablesAll Tablesdev_userdev_groupdev_group_membersprod_userprod_groupprod_group_membersdev_user-userdev_user-organizationalPersondev_user-persondev_user-topdev_user-msExchCertificateInformationdev_user-msExchBaseClassdev_user-msExchMultiMediaUserdev_user-msExchMailStoragedev_user-msExchCustomAttributesdev_user-msExchOmaUserdev_user-shadowAccountdev_user-posixAccountdev_user-msDS-CloudExtensionsdev_user-securityPrincipaldev_user-mailRecipientdev_group-groupdev_group-topdev_group-msExchIMRecipientdev_group-msExchBaseClassdev_group-msExchCustomAttributesdev_group-msExchMailStoragedev_group-posixGroupdev_group-mailRecipientdev_group-securityPrincipalprod_user-userprod_user-organizationalPersonprod_user-personprod_user-topprod_user-msExCertificateInformationprod_user-msExchBaseClassprod_user-msExchMultiMediaUserprod_user-msExchMailStorageprod_user-msExchCustomAttributesprod_user-msExchOmaUserprod_user-shadowAccountprod_user-posixAccountprod_user-msDS-CloudExtensionsprod_user-securityPrincipalprod_user-mailRecipientprod_group-groupprod_group-topprod_group-msExchIMRecipientprod_group-msExchBaseClassprod_group-msExchCustomAttributesprod_group-msExchMailStorageprod_group-posixGroupprod_group-mailRecipientprod_group-securityPrincipalEvery User tables contains the Users DisplayName and DistinguishedNameEvery Group table contains the Group DisplayName and DistinguishedNameI also created List tables so that I can narrow down work to just users or just groups or a specific environment.DynamicTablesProduction TablesDevelopment User TablesDevelopment Group TablesProduction User TablesProduction Group TablesDevelopment TablesAll TablesDynamicTablesI have created relationships from each&nbsp;prod_group-*** and prod_user-*** to the corresponding dev_group-*** and dev_user-***each ***_group-*** is connected to ***_groupeach ***_user-*** is connected to ***_user***_group-members are connected to ***_group and ***_userAll relationships except the following are inactiveAll relationships are linked to column distinguishedNameI was able to create a measure that would provide me with a comparison of the users in dev and prod using this:&nbsp;Comparison = 
VAR BothExist = 
    COUNTROWS(
        INTERSECT(
            VALUES('dev_user-user'[distinguishedName]),
            VALUES('prod_user-user'[distinguishedName])
        )
    )
VAR OnlyInProd = 
    COUNTROWS(
        EXCEPT(
            VALUES('prod_user-user'[distinguishedName]),
            VALUES('dev_user-user'[distinguishedName])
        )
    )
VAR OnlyInDev = 
    COUNTROWS(
        EXCEPT(
            VALUES('dev_user-user'[distinguishedName]),
            VALUES('prod_user-user'[distinguishedName])
        )
    )
RETURN
    CONCATENATEX (
        {
            "Both Exist: " &amp; FORMAT(BothExist, "#,##0"),
            "Only in Prod: " &amp; FORMAT(OnlyInProd, "#,##0"),
            "Only in Dev: " &amp; FORMAT(OnlyInDev, "#,##0")
        },
        [Value],
        UNICHAR (10)
    )&nbsp;&nbsp;I thought I could use the DAX query to get me more information on all the column names&nbsp;&nbsp;EVALUATE
SUMMARIZE(COLUMNSTATISTICS(),[Table Name],[Column Name])&nbsp;However, its to large of a query and excedes 1024.&nbsp;I tried to break it down into their perspective data clusters&nbsp;EVALUATE
VAR DevUsers = 
    SUMMARIZE(
        FILTER(
            COLUMNSTATISTICS(),
            LEFT([Table Name], 9) = "dev_user-"
        ),
        [Table Name],
        [Column Name]
    )
VAR ProdUsers = 
    SUMMARIZE(
        FILTER(
            COLUMNSTATISTICS(),
            LEFT([Table Name], 10) = "prod_user-"
        ),
        [Table Name],
        [Column Name]
    )
VAR DevGroups = 
    SUMMARIZE(
        FILTER(
            COLUMNSTATISTICS(),
            LEFT([Table Name], 10) = "dev_group-"
        ),
        [Table Name],
        [Column Name]
    )
VAR ProdGroups = 
    SUMMARIZE(
        FILTER(
            COLUMNSTATISTICS(),
            LEFT([Table Name], 11) = "prod_group-"
        ),
        [Table Name],
        [Column Name]
    )
RETURN
UNION(DevUsers, ProdUsers, DevGroups, ProdGroups)&nbsp;&nbsp;But it still exceeded 1024mb. I even tried to granuarly break them down blocking out 5 tables at a time, and it still exceded.&nbsp;So now I am left with trying to figure out a way to compare 2 tables at a time, however Im still not sure how to accomplish this efficiently and not have to write multiple queries and measures per pair of tables.&nbsp;Again the end goal here is to be able to determine the data that is missing in Development Environment that is in Production.

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Compare Active Directory Environments Users/Groups/Group_Memberships

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