This post discusses the challenges for the ETL to handle Late Arriving Dimensions/Early Arriving Facts. and proposes solutions to tackle these challenges and optimize the ETL for Late Arriving Dimensions. Continue reading »

So you have several cubes to build and perhaps wondering what is the best way to build and deploy your cubes. Should you build all the cubes within the same Analysis Service database or create one database per cube? While there are benefits of having all cubes in one database there are scenarios where having one cube per database might be just as helpful. The following guidelines might help you to choose the best approach to build and deploy your SSAS cubes. Continue reading »

SSAS provides Process Incremental as one of the processing options for cubes, measure groups and partitions. It is important to understand how Process Incremental works because it differs significantly from the seemingly equivalent Process Update for dimensions. Continue reading »

This post is about troubleshooting and finding a workaround for this exception - [Analysis Services Execute DDL Task] Error: Internal error: An unexpected error occurred (file 'mdinfo.cpp', line nnnn, function 'MDInfo::Init').
Continue reading »

This post is about troubleshooting and finding a workaround for this exception - [Analysis Services Execute DDL Task] Error: Internal error: An unexpected error occurred (file 'mdinfo.cpp', line nnnn, function 'MDInfo::Init').
Continue reading »

This post is about troubleshooting and finding a workaround for this exception - [Analysis Services Execute DDL Task] Error: Internal error: An unexpected error occurred (file 'mdinfo.cpp', line nnnn, function 'MDInfo::Init').
Continue reading »

'A fact relationship cannot be defined. The dimension and measure group must be based on the same table in the same data source view.' If you get this message when defining the relationship type as Fact between the measure group and Degenerate/Fact dimension, what could possibly go wrong especially when the second part of the message clearly tells what needs to happen? Continue reading »

Some coding tips when using Analysis Management Objects (AMO) methods Continue reading »

Analysis Management Objects (AMO) opens up the object model of SSAS cube for programming. With a bit of .NET programing, AMO can be effectively used to automate administration tasks. Securing the cube is one such task that can be easily automated using AMO. The rest of this post explains how to automate cube security using AMO.

A Role Playing Dimensions reuses its definition to represent same concept in different contexts. Common example of Role Playing Dimension is date dimension. For instance, date dimension can be used to represent order date, delivery date and shipment date. Currency, staff, organisation hierarchy are other dimensions that are usually role played. While Role Playing Dimensions promote dimension reuse and share dimension data security, the effect on cube browsing must also be considered.

SSAS Dynamic Management Views (DMV's) are very useful to query metadata of a cube. For developers, this is quite handy to provide simple documentation of the cubes they build.

Dataflow Discoverer (DFLD) is a command-line discovery utility that detects and writes the metadata of SSIS dataflow columns to a SQL Server lookup table. DFLD detects Dataflows nested within containers up to any level of nesting.DFLD works on one package at a time. It's a typical ops requirement to keep the DFLMetadata of all SSIS packages up to date. In this post a technique is described to automate refresh of dataflow column metadata.

SSIS automatically creates two columns on the Error Output from dataflow components - ErrorColumn and ErrorCode. ErrorColumn contains the column ID of the erroneous column. Column ID is a number that is unique to a dataflow within the package. Similarly ErrorCode contains the error number that caused the row to be rejected. At the moment, SSIS by default does not provide ErrorColumn name and ErrorCode description. This post describes a technique to get the name of ErrorColumn.

Dataflow Discoverer (DFLD) is a command-line discovery utility that detects and writes the metadata of SSIS dataflow columns to a SQL Server lookup table. DFLD detects Dataflows nested within containers up to any level of nesting.

This post highlights the differences between Analysis Services and PowerPivot in 30 seconds.

If you recently upgraded to SQL Server 2008 or considering an upgrade, you would be glad to know that the Deployment Wizard of Analysis Services works in the way it should.Roles retain their existing permission to cubes and mining structures after deployment. Existing permissions are not lost. Dimension Data and Cell Data permissions are preserved in the role definition after deployment. And Role membership stays put.

Analysis Service supports role based model to secure cube data. Data can be secured at object level, cell level (Cell Data Access) and at member level (Dimension Data Access). A user can be a member of more than one role and it is important to understand the behaviour of Analysis Services role permissions in such a scenario. Analysis Services role permissions are additive in nature. This means if you are a member of a role that has been granted access to an Analysis Service object; you have access to that object even if you belong to another role which denies access to the same object. In other word the least restrictive roles permissions override the most restrictive role permissions. This could throw some nasty surprises that you never expected.

The greatest strength of PowerPivot is the ability to create relationship with different sources of data and piggyback on this relationship for various kinds of analysis. PowerPivot relationship requires one-to-many relationship between the parent and child tables. But what happens if the relationship between parent and child tables is many-to-many instead? For e.g. you might want to establish a relationship between a fact table that has a Business Key to the dimension table and that dimension table maintains history. In this post I explain how to establish a relationship between two tables that do not exhibit one-to-many relationship on their Business Key using a technique that is similar to an ETL Type 2 SCD lookup using Data Analysis Expressions (DAX).

Pivoting is typically used in DW solutions to load multi-valued dimensions. In my earlier post, I demonstrated how SSIS Pivot component works and the advantages of using SSIS Pivot component over its equivalent T-SQL Pivot statement. In this post, I explain a technique to Pivot data when Pivot Column is not explicitly available.

Pivot transformation converts multiple rows of data into a single record by using an input column that has a finite set of distinct values. Pivoting is typically used in DW solutions to handle multi-valued dimensions. In SQL Server, Pivoting is done using either a) T-SQL Pivot statement or b) SSIS Pivot Transformation .There are two significant benefits that stand-out in favour of using SSIS Pivot Transformation for ETL scenarios. The remainder of this post explains just that.