So long AdventureWorks, and hello Wide World Importers!

Any future development on Microsoft sample databases should use Wide World Importers instead of AdventureWorks since it will not be updated or maintained after 2016.

The Wide World Importers can be downloaded from here.

Can I still use AdventureWorks with SQL Server 2016?

Yes, the final AdventureWorks build will be updated on the official download site here and you can still use AdventureWorks with your SQL Server 2016 instance.

Microsoft Reference

What is Slowly Changing Dimension

A data warehouse dimensional model comprises of facts and measures defined in the context of their dimensions.
Dimensions and their attributes, are relatively constant, but they do change over time.

The term Slowly Changing Dimension (SCD) is about tracking the variation in dimensional attributes over time.
The word slowly in this context might seem incorrect.
For example, a salesperson or store dimension, might change rapidly if a company reorganizes.
But in general, when compared to a measure in a fact table, changes to dimensional data occur slowly.

Within a data warehouse, you can handle slowly changing dimensions in several ways.

Type 0 – The passive method
In this method no special action is performed upon dimensional changes.
Some dimension data can remain the same as it was first time inserted, others may be overwritten.

Type 1 (overwrite):
A Type One change overwrites an existing dimensional attribute with new information.
In the customer name-change example, the new name overwrites the old name, and the value for the old version is lost.
A Type One change updates only the attribute, doesn’t insert new records, and affects no keys.
No history information is stored. Existing data is overridden by new values.

Before the change:

Customer_ID Customer_Name Customer_Type
1 Cust_1 Corporate

After the change:

Customer_ID Customer_Name Customer_Type
1 Cust_1 Retail

Type 2 (add a row):
A Type Two change writes a record with the new attribute information and preserves a record of the old dimensional data.
Type Two changes let us preserve historical data.
The history of data changes is preserved.
A new record is inserted each time a change is made.
Every data row has a valid from date and valid to date indicating the time period of the data’s validity, and each row usually has as isCurrent type of field that is set to Yes for the active record with the others set to No.
When a fact table record is inserted, it will be given the appropriate surrogate key of the dimension record.
Since Type Two changes add records, they can increase the database’s size.

Before the change:

Customer_ID Customer_Name Customer_Type Start_Date End_Date Current_Flag
1 Cust_1 Corporate 22-07-2010 31-12-9999 Y

After the change:

Customer_ID Customer_Name Customer_Type Start_Date End_Date Current_Flag
1 Cust_1 Corporate 22-07-2010 17-05-2012 N
2 Cust_1 Retail 18-05-2012 31-12-9999 Y

Type 3 (add a column):
This method traces changes using separate columns (but no new rows).
This means there is a limit to history preservation based on the number of columns in each row that are designated for storing historical data.
For example, a record may have the fields Territory1, Territory1EffectiveDate, Territory2, Territory2EffectiveDate, etc.
Type Three changes is implemented only if we have a limited need to preserve and accurately describe history, such as when someone gets married and you need to retain the previous name.
Instead of creating a new dimensional record to hold the attribute change, a Type Three change places a value for the change in the original dimensional record.
We can create multiple fields to hold distinct values for separate points in time.
In the case of a name change, you could create an OLD_NAME and NEW_NAME field and a NAME_CHANGE_EFF_DATE field to record when the change occurs.
This method preserves the change.
But how would we handle a second name change, or a third, and so on?
The side effects of this method are increased table size and, more important,
increased complexity of the queries that analyze historical values from these old fields.
After more than a couple of iterations, queries become impossibly complex, and ultimately you’re constrained by the maximum number of attributes allowed on a table.

Before the change:

Customer_ID Customer_Name Current_Type Previous_Type
1 Cust_1 Corporate Corporate

After the change:

Customer_ID Customer_Name Current_Type Previous_Type
1 Cust_1 Retail Corporate

Type 4 – Using historical table.
In this method a separate historical table is used to track all dimension’s attribute historical changes for each of the dimension.

Current table:

Customer_ID Customer_Name Customer_Type
1 Cust_1 Corporate

Historical table:

Customer_ID Customer_Name Customer_Type Start_Date End_Date
1 Cust_1 Retail 01-01-2010 21-07-2010
1 Cust_1 Oher 22-07-2010 17-05-2012
1 Cust_1 Corporate 18-05-2012 31-12-9999

Type 6 – Combine approaches of types 1,2,3 (1+2+3=6). In this type we have in dimension table such additional columns as:
current_type – for keeping current value of the attribute. All history records for given item of attribute have the same current value.
historical_type – for keeping historical value of the attribute. All history records for given item of attribute could have different values.
start_date – for keeping start date of ‘effective date’ of attribute’s history.
end_date – for keeping end date of ‘effective date’ of attribute’s history.
current_flag – for keeping information about the most recent record.

In this method to capture attribute change we add a new record as in type 2.
The current_type information is overwritten with the new one as in type 1.
We store the history in a historical_column as in type 3.
The ‘main’ dimension table keeps only the current data e.g. customer and customer_history tables.

Customer_ID Customer_Name Current_Type Historical_Type Start_Date End_Date Current_Flag
1 Cust_1 Corporate Retail 01-01-2010 21-07-2010 N
2 Cust_1 Corporate Other 22-07-2010 17-05-2012 N
3 Cust_1 Corporate Corporate 18-05-2012 31-12-9999 Y


SQLMag Reference

Datawarehouse4u Reference

jamesserra blog reference

How to SSAS – SQL Server Analysis Services to generate memory dump files

In the config file msmdsrv located at “Program Files\Microsoft SQL Server\MSAS11.MULTIDIM\OLAP\Config”.

Make sure CreateAndSendCrashReports is set to at least 1.

To configure SSAS to generate a full dump file that includes the handle information, we can set the SQLDumperFlagsOn setting to 0x34 and the MiniDumpFlagsOn setting to 0x4.

For example, the Exception section in the Msmdsrv.ini file may resemble the following:

<MinidumpErrorList>0xC1000000, 0xC1000001, 0xC1000016, 0xC11D0005, 0xC102003F</MinidumpErrorList>

Reference from

Unable to process SSAS cube – error – OLE DB error: OLE DB or ODBC error: A network-related or instance-specific error has occurred while establishing a connection to SQL Server.

Today we deployed one cube from Production to Development. On processing it we received error:

OLE DB error: OLE DB or ODBC error: A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online.; 08001; Client unable to establish connection; 08001; Encryption not supported on the client.; 08001

Even though we had never changed the provider (the default setup gave us SQL native client), based on these 2 blogs (blog1blog2) we changed it to OLE DB. This simple change allowed the cube to process successfully again.

How to kill or disconnect session or users on SSAS server

There are multiple ways to do this. We will explore using hybrid approach as mentioned in this Microsoft article.

  1. In SQL Server Management Studio, connect to an Analysis Services instance.
  2. Paste any one of the following DMV queries in an MDX query window to get a list of all sessions, connections, and commands that are currently executing:

    Select * from $System.Discover_Sessions

    Select * from $System.Discover_Connections

    Select * from $System.Discover_Commands

  3. Press F5 to execute the query.

    The DMV query returns session and connection information in a tabular result set that is easier read and copy from.

    Keep the query window open. In the next step, you will want to return to this page to copy the SPIDs of the session you want to disconnect.

    To end a session, open a second XMLA query window.

  4. Paste the following syntax into an MDX query window, replacing the ConnectionID, SessionID, or SPID placeholder with a valid value copied from the previous step.

<Cancel xmlns=””&gt;


<Cancel xmlns=””><ConnectionID>111</ConnectionID&gt;


Thus you can terminate the long running queries or memory hogging sessions.