data-warehouse

I'd like to ask your input on what the best practice is for handling null or empty data values when it pertains to data warehousing and SSIS/SSAS. I have several fact and dimension tables that contain null values in different rows. Specifics: 1) What is the best way to handle null date/times values? Should I make a 'default' row in my time or date dimensions and point SSIS to the default row when there is a null found? 2) What is the best way to handle nulls/empty values inside of dimension data. Ex: I have some rows in an 'Accounts' dimensions that have empty (not NULL) values in the Account

I'm just starting to get my head around MDX and I'm having trouble with a calculated member. I'm using the following MDX: IIF( ISEMPTY((Axis(1).Item(0).Item(0).Dimension.CurrentMember, [Measures].[Qty])) ,NULL ,([Product].[Product Code].CurrentMember.Parent, [Measures].[Qty]) ) What I'm trying to do is get a total quantity of the group of products displayed in a cube. I then use that total to divide by each product's quantity to get a "percent of total" measure. The above MDX does correctly return the total quantity of products displayed in any dimension. However, when a user in Excel changes

When reading about star schema design I have seen that many people uses various names for different types of dimension tables. Please list the names and a small description of each type. If any list also an alias name. I have come across these types of dimension tables so far: Regular dimension Standard star dimension. Time Dimension A special case of the standard star dimension. Parent-child dimension Used to model hierarchical structures, fx BOM (bill of materials). Snowflake dimension Can also be used to model hierarchical structures. Degenerate dimensions When the dimension attribute is

I have developed an Informatica PowerDesigner 9.1 ETL Job which uses lookup and an update transform to detect if the target table has the the incoming rows from the source or not. I have set for the Update transform a condition IIF(ISNULL(target_table_surrogate_id), DD_INSERT, DD_REJECT) Now, when the incoming row is already in the target table, the row is rejected. Informatica writes these rejected rows into a .bad file. How to prevent this? Is there a way to determine that the rejected rows are not written into a .bad file? Or should I use e.g. a router insted of an update transform to

I am looking for SCD1 and SCD2 implementation in Hive (1.2.1). I am aware of the workaround to load SCD1 and SCD2 tables prior to Hive (0.14). Here is the link for loading SCD1 and SCD2 with the workaround approach http://hortonworks.com/blog/four-step-strategy-incremental-updates-hive/ Now that Hive supports ACID operations just want to know if there is a better or direct way of loading it. As HDFS is immutable storage it could be argued that versioning data and keeping history (SCD2) should be the default behaviour for loading dimensions. You can create a View in your Hadoop SQL query engine