Dimension Table populating data

Hi
I am in the process of creating a data mart with a star schema.
The star schema has been defined with the fact and dimension tables and the primary and foreign keys.
I have written the script for one of the dimensions and would like to know when the job runs on a daily basis should the job truncate the table every day and rebuild the dimension table or should it only add new records to the table. If it should add only
new records to the table how do is this done?
I assume that the fact table job is run once a day and only new data is added to it?
Thanks

It will depend on the volume of your dimensions. In most of our projects, we do not truncate, we update only updated rows based on a fingerprint (to make the comparison faster than column by column), and insert for new rows (SCD1). For SCD2 we apply
similar approach for updates and inserts, and expirations in batch (one UPDATE for all applicable rows at the end of the package/ETL).
If your dimension is very large, you can consider truncating all data or deleting only affected modified rows (based on nosiness key) to later reload those, but you have to be carefully maintaining the same surrogate keys reference by your
existing facts.
HTH,
Please, mark this post as Answer if this helps you to solve your question/problem.
Alan Koo | "Microsoft Business Intelligence and more..."
http://www.alankoo.com

Similar Messages

Reg: Fact table and Dimension table in Data Warehousing -

Hi Experts,
I'm not exactly getting the difference between the criteria which decide how to create a Fact table and Dimension table.
This link http://stackoverflow.com/questions/9362854/database-fact-table-and-dimension-table states :
Fact table contains data that can be aggregate.
Measures are aggregated data expressions (e. Sum of costs, Count of calls, ...)
Dimension contains data that is use to generate groups and filters.
This's fine but how does one decide which columns to consider for Fact table and which columns for Dimension table?
Any help is much appreciated.
Pardon me if this's not the correct place for this question. My first question in the new forum.
Thanks and Regards,
Ranit Biswas

ranitB wrote:
But my main doubt was - what is the criteria to differentiate between columns for Fact tables and Dimension tables? How can one decide upon the design?
Columns of a fact table will often be 'scalar' attributes of the 'fact' data item. A dimension table will often be 'compound' attributes of a 'fact'.
Consider employee information. The EMPLOYEE table can be a fact table. It might have scalar attribute columns such as: DATE_HIRED, STATUS, EMPLOYEE_ID, and so on.
Other related information that can't be specified as a single attribute value would often be stored in a 'dimension' table: ADDRESS, PHONE_NUMBER.
Each address requires several columns to define it: ADDRESS1, ADDRESS2, CITY, STATE, ZIP, COUNTRY. And an employee might have several addresses: WORK_ADDRESS, HOME_ADDRESS. That address info would be stored in a 'dimension' table and only the primary key value of the address record would be stored in the EMPLOYEE 'fact' table.
Same with PHONE_NUMBER. Several columns are required to define a phone number and each employee might have several of them. The dimension tables are used to help 'normalize' the data in the employee 'fact' table.
And that EMPLOYEE table might also be a DIMENSION table for other FACT tables. A DEVELOPER table might have an EMPLOYEE_ID column with a value that points to a 'dimension' row in the EMPLOYEE dimension table.

Dimension table design Data modeling question

Hi Experts,
Sorry if I am putting my question in a wrong forum and please suggest an appropriate forum.
need your opinion on the existing design of our 10 years old datawarehouse.
There is one dimension table with structure like following
Dimension Table
Dimension Key Number (THIS IS NOT A PRIMARY KEY)
Natural key (from source) Number
source name character
current record indicator char(1)
form_date date
to_date date
many other columns, which if change a new current record is created and previous is marked as H-historical
Data is stored in the dimension table like this
Dimension_key Natural key Source Name current record ind from_date to_date
1 10001 Source1 H 1-jan-2005 31-may-2005
1 10001 Source1 H 1-jun-20005 12-dec-2011
1 10001 Source1 C 13-dec-2011 NULL
2 20002 Source1 H 1-jun-20001 12-dec-2011
2 20002 Source1 C 13-dec-2011 NULL
The problem I see in this design is that there is no surrogate key, if any attribute is changed the new record is inserted by first taking the dimension key based on the (natural_key,source_name,current_record_ind).
Shouldn't it be stored like following based on the data-warehousing principals.
Dimension_key Natural key Source Name current record ind from_date to_date
1 10001 Source1 H 1-jan-2005 31-may-2005
2 10001 Source1 H 1-jun-20005 12-dec-2011
3 10001 Source1 C 13-dec-2011 NULL
4 20002 Source1 H 1-jun-20001 12-dec-2011
5 20002 Source1 C 13-dec-2011 NULL
Please let me know the pros and cons of the current design.

And what if you have both the features something like this :
Lineno Dimension_key Natural key Source Name current record ind    from_date         to_date
     1              1       10001     Source1                  H   1-jan-2005     31-may-2005
     2              1       10001     Source1                  H 1-jun-20005     12-dec-2011
     3              1       10001     Source1                  C 13-dec-2011            NULLI mean just add a new column and populate it with required order by clause. Because what i guess, that in the second example you just added a new column which is something like a line number.
Regards
Girish Sharma

Why do dimension tables contain data?

Hi there,
as far as I understodd the BW datamodel, the dimensions are to summon characteristics that do belong to the specific view I want to access via my InfoCube. So, the dimensional tables should contain just the mapping between DimIDs and SID, shouldn't they? What else do these dimensional tables contain??
Thanks,
Pascal

Hi,
I think the concept of using dimension table as the intermediate to SID And Facttable is to develope <i>extended star schema</i>. So that it allowing more than 13 characterstics in infocube.
With rgds,
Anil Kumar Sharma .P

Pre-loading a table, populating data

hopefully someone has already crossed this bridge,
does anyone know how to pre-load a table and then populate the data returned from a query? I have seen websites like ingrammicro.com that display the results from a search where you can see the table and some of the data , and then slowly you start seeing pricing populated within the table. maybe this is more of an HTML question.not sure. thanks for any information you can give.
thanks, Tim

yes, that will work. thanks.

Difference between Data staging and Dimension Table ?

Difference between Data staging and Dimension Table ?

Data Staging:
Data extraction and transformation is done here.
Meaning that, if we have source data in flat file, we extract it and load into staging tables, we take care of nulls, we change datetime format etc.. and after such cleansing/transformation at then end, load it to Dim/Fact tables
Pros: Makes process simpler and easy and also we can keep track of data as we have data in staging
Cons: Staging tables need space hence need memory space
Dimension Table:
tables which describes/stores the attribute about specific objects
Below is star schema which has dimension storing information related to Product, Customer etc..
-Vaibhav Chaudhari

Foreign keys in SCD2 dimensions and fact tables in data warehouse

Hello.
I have datawarehouse in snowflake schema. All dimensions are SCD2, the columns are like that:
ID (PK) SID NAME ... START_DATE END_DATE IS_ACTUAL
1 1 XXX 01.01.2000 01.01.2002 0
2 1 YYX 02.01.2002 01.01.2004 1
3 2 SYX 02.01.2002 1
4 3 AYX 02.01.2002 01.01.2004 0
5 3 YYZ 02.01.2004 1
On this table there are relations from other dimension and fact table.
Need I create foreign keys for relation?
And if I do, on what columns? SID (serial ID) is not unique. If I create on ID, I have to get SID and actual row in any query.

>
I have datawarehouse in snowflake schema. All dimensions are SCD2, the columns are like that:
ID (PK) SID NAME ... START_DATE END_DATE IS_ACTUAL
1 1 XXX 01.01.2000 01.01.2002 0
2 1 YYX 02.01.2002 01.01.2004 1
3 2 SYX 02.01.2002 1
4 3 AYX 02.01.2002 01.01.2004 0
5 3 YYZ 02.01.2004 1
On this table there are relations from other dimension and fact table.
Need I create foreign keys for relation?
>
Are you still designing your system? Why did you choose NOT to use a Star schema? Star schema's are simpler and have some performance benefits over snowflakes. Although there may be some data redundancy that is usually not an issue for data warehouse systems since any DML is usually well-managed and normalization is often sacrificed for better performance.
Only YOU can determine what foreign keys you need. Generally you will create foreign keys between any child table and its parent table and those need to be created on a primary key or unique key value.
>
And if I do, on what columns? SID (serial ID) is not unique. If I create on ID, I have to get SID and actual row in any query.
>
I have no idea what that means. There isn't any way to tell from just the DDL for one dimension table that you provided.
It is not clear if you are saying that your fact table will have a direct relationship to the star-flake dimension tables or only link to them through the top-level dimensions.
Some types of snowflakes do nothing more than normalize a dimension table to eliminate redundancy. For those types the dimension table is, in a sense, a 'mini' fact table and the other normalized tables become its children. The fact table only has a relation to the main dimension table; any data needed from the dimensions 'child' tables is obtained by joining them to their 'parent'.
Other snowflake types have the main fact table having relations to one or more of the dimensions 'child' tables. That complicates the maintenance of the fact table since any change to the dimension 'child' table impacts the fact table also. It is not recommended to use that type of snowflake.
See the 'Snowflake Schemas' section of the Data Warehousing Guide
http://docs.oracle.com/cd/B28359_01/server.111/b28313/schemas.htm
>
Snowflake Schemas
The snowflake schema is a more complex data warehouse model than a star schema, and is a type of star schema. It is called a snowflake schema because the diagram of the schema resembles a snowflake.
Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a product dimension table in a star schema might be normalized into a products table, a product_category table, and a product_manufacturer table in a snowflake schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance. Figure 19-3 presents a graphical representation of a snowflake schema.

Fact and dimension table partition

My team is implementing new data-warehouse. I would like to know that when should we plan to do partition of fact and dimension table, before data comes in or after?

Hi,
It is recommended to partition Fact table (Where we will have huge data). Automate the partition so that each day it will create a new partition to hold latest data (Split the previous partition into 2). Best practice is to create partition on transaction
timestamps so load the incremental data into a empty table called (Table_IN) and then Switch that data into main table (Table). Make sure your tables (Table and Table_IN) should be on one file group.
Refer below content for detailed info
Designing and Administrating Partitions in SQL Server 2012
A popular method of better managing large and active tables and indexes is the use of partitioning. Partitioning is a feature for segregating I/O workload within
SQL Server database so that I/O can be better balanced against available I/O subsystems while providing better user response time, lower I/O latency, and faster backups and recovery. By partitioning tables and indexes across multiple filegroups, data retrieval
and management is much quicker because only subsets of the data are used, meanwhile ensuring that the integrity of the database as a whole remains intact.
Tip
Partitioning is typically used for administrative or certain I/O performance scenarios. However, partitioning can also speed up some queries by enabling
lock escalation to a single partition, rather than to an entire table. You must allow lock escalation to move up to the partition level by setting it with either the Lock Escalation option of Database Options page in SSMS or by using the LOCK_ESCALATION option
of the ALTER TABLE statement.
After a table or index is partitioned, data is stored horizontally across multiple filegroups, so groups of data are mapped to individual partitions. Typical
scenarios for partitioning include large tables that become very difficult to manage, tables that are suffering performance degradation because of excessive I/O or blocking locks, table-centric maintenance processes that exceed the available time for maintenance,
and moving historical data from the active portion of a table to a partition with less activity.
Partitioning tables and indexes warrants a bit of planning before putting them into production. The usual approach to partitioning a table or index follows these
steps:
1. Create
the filegroup(s) and file(s) used to hold the partitions defined by the partitioning scheme.
2. Create
a partition function to map the rows of the table or index to specific partitions based on the values in a specified column. A very common partitioning function is based on the creation date of the record.
3. Create
a partitioning scheme to map the partitions of the partitioned table to the specified filegroup(s) and, thereby, to specific locations on the Windows file system.
4. Create
the table or index (or ALTER an existing table or index) by specifying the partition scheme as the storage location for the partitioned object.
Although Transact-SQL commands are available to perform every step described earlier, the Create Partition Wizard makes the entire process quick and easy through
an intuitive point-and-click interface. The next section provides an overview of using the Create Partition Wizard in SQL Server 2012, and an example later in this section shows the Transact-SQL commands.
Leveraging the Create Partition Wizard to Create Table and Index Partitions
The Create Partition Wizard can be used to divide data in large tables across multiple filegroups to increase performance and can be invoked by right-clicking
any table or index, selecting Storage, and then selecting Create Partition. The first step is to identify which columns to partition by reviewing all the columns available in the Available Partitioning Columns section located on the Select a Partitioning Column
dialog box, as displayed in Figure 3.13. This screen also includes additional options such as the following:
Figure 3.13. Selecting a partitioning column.
The next screen is called Select a Partition Function. This page is used for specifying the partition function where the data will be partitioned. The options
include using an existing partition or creating a new partition. The subsequent page is called New Partition Scheme. Here a DBA will conduct a mapping of the rows selected of tables being partitioned to a desired filegroup. Either a new partition scheme should
be used or a new one needs to be created. The final screen is used for doing the actual mapping. On the Map Partitions page, specify the partitions to be used for each partition and then enter a range for the values of the partitions. The
ranges and settings on the grid include the following:
Note
By opening the Set Boundary Values dialog box, a DBA can set boundary values based on dates (for example, partition everything in a column after a specific
date). The data types are based on dates.
Designing table and index partitions is a DBA task that typically requires a joint effort with the database development team. The DBA must have a strong understanding
of the database, tables, and columns to make the correct choices for partitioning. For more information on partitioning, review Books Online.
Enhancements to Partitioning in SQL Server 2012
SQL Server 2012 now supports as many as 15,000 partitions. When using more than 1,000 partitions, Microsoft recommends that the instance of SQL Server have at
least 16Gb of available memory. This recommendation particularly applies to partitioned indexes, especially those that are not aligned with the base table or with the clustered index of the table. Other Data Manipulation Language statements (DML) and Data
Definition Language statements (DDL) may also run short of memory when processing on a large number of partitions.
Certain DBCC commands may take longer to execute when processing a large number of partitions. On the other hand, a few DBCC commands can be scoped to the partition
level and, if so, can be used to perform their function on a subset of data in the partitioned table.
Queries may also benefit from a new query engine enhancement called partition elimination. SQL Server uses partition enhancement automatically if it is available.
Here’s how it works. Assume a table has four partitions, with all the data for customers whose names begin with R, S, or T in the third partition. If a query’s WHERE clause
filters on customer name looking for ‘System%’, the query engine knows that it needs only to partition three to answer
the request. Thus, it might greatly reduce I/O for that query. On the other hand, some queries might take longer if there are more than 1,000 partitions and the query is not able to perform partition elimination.
Finally, SQL Server 2012 introduces some changes and improvements to the algorithms used to calculate partitioned index statistics. Primarily, SQL Server 2012
samples rows in a partitioned index when it is created or rebuilt, rather than scanning all available rows. This may sometimes result in somewhat different query behavior compared to the same queries running on SQL Server 2012.
Administrating Data Using Partition Switching
Partitioning is useful to access and manage a subset of data while losing none of the integrity of the entire data set. There is one limitation, though. When
a partition is created on an existing table, new data is added to a specific partition or to the default partition if none is specified. That means the default partition might grow unwieldy if it is left unmanaged. (This concept is similar to how a clustered
index needs to be rebuilt from time to time to reestablish its fill factor setting.)
Switching partitions is a fast operation because no physical movement of data takes place. Instead, only the metadata pointers to the physical data are altered.
You can alter partitions using SQL Server Management Studio or with the ALTER TABLE...SWITCH
Transact-SQL statement. Both options enable you to ensure partitions are
well maintained. For example, you can transfer subsets of data between partitions, move tables between partitions, or combine partitions together. Because the ALTER TABLE...SWITCH statement
does not actually move the data, a few prerequisites must be in place:
• Partitions must use the same column when switching between two partitions.
• The source and target table must exist prior to the switch and must be on the same filegroup, along with their corresponding indexes,
index partitions, and indexed view partitions.
• The target partition must exist prior to the switch, and it must be empty, whether adding a table to an existing partitioned table
or moving a partition from one table to another. The same holds true when moving a partitioned table to a nonpartitioned table structure.
• The source and target tables must have the same columns in identical order with the same names, data types, and data type attributes
(length, precision, scale, and nullability). Computed columns must have identical syntax, as well as primary key constraints. The tables must also have the same settings for ANSI_NULLS and QUOTED_IDENTIFIER properties.
Clustered and nonclustered indexes must be identical. ROWGUID properties
and XML schemas must match. Finally, settings for in-row data storage must also be the same.
• The source and target tables must have matching nullability on the partitioning column. Although both NULL and NOT
NULL are supported, NOT
NULL is strongly recommended.
Likewise, the ALTER TABLE...SWITCH statement
will not work under certain circumstances:
• Full-text indexes, XML indexes, and old-fashioned SQL Server rules are not allowed (though CHECK constraints
are allowed).
• Tables in a merge replication scheme are not allowed. Tables in a transactional replication scheme are allowed with special caveats.
Triggers are allowed on tables but must not fire during the switch.
• Indexes on the source and target table must reside on the same partition as the tables themselves.
• Indexed views make partition switching difficult and have a lot of extra rules about how and when they can be switched. Refer to
the SQL Server Books Online if you want to perform partition switching on tables containing indexed views.
• Referential integrity can impact the use of partition switching. First, foreign keys on other tables cannot reference the source
table. If the source table holds the primary key, it cannot have a primary or foreign key relationship with the target table. If the target table holds the foreign key, it cannot have a primary or foreign key relationship with the source table.
In summary, simple tables can easily accommodate partition switching. The more complexity a source or target table exhibits, the more likely that careful planning
and extra work will be required to even make partition switching possible, let alone efficient.
Here’s an example where we create a partitioned table using a previously created partition scheme, called Date_Range_PartScheme1.
We then create a new, nonpartitioned table identical to the partitioned table residing on the same filegroup. We finish up switching the data from the partitioned table into the nonpartitioned table:
CREATE TABLE TransactionHistory_Partn1 (Xn_Hst_ID int, Xn_Type char(10)) ON Date_Range_PartScheme1 (Xn_Hst_ID) ; GO CREATE TABLE TransactionHistory_No_Partn (Xn_Hst_ID int, Xn_Type
char(10)) ON main_filegroup ; GO ALTER TABLE TransactionHistory_Partn1 SWITCH partition1 TO TransactionHistory_No_Partn; GO
The next section shows how to use a more sophisticated, but very popular, approach to partition switching called a sliding
window partition.
Example and Best Practices for Managing Sliding Window Partitions
Assume that our AdventureWorks business is booming. The sales staff, and by extension the AdventureWorks2012 database, is very busy. We noticed over time that
the TransactionHistory table is very active as sales transactions are first entered and are still very active over their first month in the database. But the older the transactions are, the less activity they see. Consequently, we’d like to automatically group
transactions into four partitions per year, basically containing one quarter of the year’s data each, in a rolling partitioning. Any transaction older than one year will be purged or archived.
The answer to a scenario like the preceding one is called a sliding window partition because
we are constantly loading new data in and sliding old data over, eventually to be purged or archived. Before you begin, you must choose either a LEFT partition function window or a RIGHT partition function window:
1. How
data is handled varies according to the choice of LEFT or RIGHT partition function window:
• With a LEFT strategy, partition1 holds the oldest data (Q4 data), partition2 holds data that is 6- to 9-months old (Q3), partition3
holds data that is 3- to 6-months old (Q2), and partition4 holds recent data less than 3-months old.
• With a RIGHT strategy, partition4 holds the holds data (Q4), partition3 holds Q3 data, partition2 holds Q2 data, and partition1
holds recent data.
• Following the best practice, make sure there are empty partitions on both the leading edge (partition0) and trailing edge (partition5)
of the partition.
• RIGHT range functions usually make more sense to most people because it is natural for most people to to start ranges at their lowest
value and work upward from there.
2. Assuming
that a RIGHT partition function windows is used, we first use the SPLIT subclause of the ALTER PARTITION FUNCTIONstatement
to split empty partition5 into two empty partitions, 5 and 6.
3. We
use the SWITCH subclause
of ALTER TABLE to
switch out partition4 to a staging table for archiving or simply to drop and purge the data. Partition4 is now empty.
4. We
can then use MERGE to
combine the empty partitions 4 and 5, so that we’re back to the same number of partitions as when we started. This way, partition3 becomes the new partition4, partition2 becomes the new partition3, and partition1 becomes the new partition2.
5. We
can use SWITCH to
push the new quarter’s data into the spot of partition1.
Tip
Use the $PARTITION system
function to determine where a partition function places values within a range of partitions.
Some best practices to consider for using a slide window partition include the following:
• Load newest data into a heap, and then add indexes after the load is finished. Delete oldest data or, when working with very large
data sets, drop the partition with the oldest data.
• Keep an empty staging partition at the leftmost and rightmost ends of the partition range to ensure that the partitions split when
loading in new data, and merge, after unloading old data, do not cause data movement.
• Do not split or merge a partition already populated with data because this can cause severe locking and explosive log growth.
• Create the load staging table in the same filegroup as the partition you are loading.
• Create the unload staging table in the same filegroup as the partition you are deleting.
• Don’t load a partition until its range boundary is met. For example, don’t create and load a partition meant to hold data that is
one to two months older before the current data has aged one month. Instead, continue to allow the latest partition to accumulate data until the data is ready for a new, full partition.
• Unload one partition at a time.
• The ALTER TABLE...SWITCH statement
issues a schema lock on the entire table. Keep this in mind if regular transactional activity is still going on while a table is being partitioned.
Thanks Shiven:) If Answer is Helpful, Please Vote

Populating Fact and Dimension Tables

Hi there,
New to OLAP. I've defined my schema design and would like to start population my dimension tables along with the foreign key references in my fact tables to my dimension tables.
Before I begin writing a bunch of PL/SQL scripts to do this. I am wondering if any tools exist to help expedite this type of process.
As a start I will be linking my fact table to two dimensions. One being a standard date dimension, the other being a user dimension consisting of hours and minutes. Any examples, links would be greatly appreciated.
Thanks!

Hi Chandra, it would be useful if you read a little bit about BI before start working with a BI tool.
Anyway, I'll try to help you.
A dimension is a structure that will be join to the fact table to so that users can analyze data from different point of views and levels. The fact table will most probably (not always) have data at the lowest possible level. So, let's suppose you have SALES data for different cities in a country (USA). Your fact table will be:
CITY --- SalesValue
Miami --- 100
NYC --- 145
Los Angeles --- 135 (because Arnold is not managing the State very well ;-) )
You will then can have a Dimension table with the "Country" structure. This is, a table containing the different cities along with their states, counties, and finally the country. So your dimension table would look like:
NATION --- STATE --- COUNTY --- CITY
USA --- Florida --- Miami --- Miami
USA --- NY ---- NY --- NYC
USA --- Los Angeles --- LA --- Los Angeles
This dimension will allow you to aggregate the data at dffirent lavels. This is, the user will not only be able to see data at the lowest level, but also at the County, State and Country level.
You will join your fact table (field CITY) with your dimension table (field CITY). The tool will then help you with the aggregation of the values.
Ihope this helps.
J.-

How to show current date in a column of dimension table in ssas

Hi,
I have 2 dimension tables (ALLWORK and YOURWORK) and 1 measure table (STATS). In ALLWORK dim table, I have 4 columns, one of them is END_DATE. Based on END_DATE and current date I want to create a named calculation field. To achieve this, I
want to show the current date in 5th column (of ALLWORK dim table) as calculated member (as named calculation wont show me correct date).
Problem is, I am not able to create the column in the dimension table. While creating the calculated member, I am not able to select the ALLWORK dimension table, it always selects MEASURE by default. Please help.
thanks
Tarique
thanks and regards Tarique Aslam

Hi Tarique,
According to your description, you want to add a column to your dimension to show the current date, right?
In data source view, we can add named calculation to your table. A named calculation is a SQL expression represented as a calculated column. This expression appears and behaves as a column in the table. A named calculation lets you extend the relational
schema of existing tables or views in a data source view without modifying the tables or views in the underlying data source.
Reference
http://msdn.microsoft.com/en-in/library/ms174859.aspx
http://devmau5.wordpress.com/2010/03/25/the-getdate-of-mdx/
If I have anything misunderstand, please point it out.
Regards,
Charlie Liao
TechNet Community Support

F4 help for custom table field - to be used when populating data thru SM30

Hi,
I have a custom table with 5 fields - say A, B, C, D and E. While populating data to the table through SM30, I need to create a F4 help for the field C. A custom function module needs to be used.
I have created a module for the same in the event PROCESS ON VALUE-REQUEST of the function group of the table.
But the F4 for field C depends on the values put in fields A and B.
I am not able to get the values of fields A and B from within the module PROCESS ON VALUE-REQUEST.
Please help me to create the F4 help.

hii,
This is the piece of code i have used in one of my SM30 to get f4. mopdify according to ur need and use.
revert back for further help.
w_dynpread-fieldname = 'ZSITEDCDATA-SITE'.
APPEND w_dynpread TO i_dynpread.
CALL FUNCTION 'DYNP_VALUES_READ'
    EXPORTING
      dyname               = 'SAPLZSITEDCDATA'
      dynumb               = sy-dynnr
      translate_to_upper   = 'X'
    TABLES
      dynpfields           = i_dynpread
    EXCEPTIONS
      invalid_abapworkarea = 1
      invalid_dynprofield = 2
      invalid_dynproname   = 3
      invalid_dynpronummer = 4
      invalid_request      = 5
      no_fielddescription = 6
      invalid_parameter    = 7
      undefind_error       = 8
      double_conversion    = 9
      stepl_not_found      = 10
      OTHERS               = 11.
IF sy-subrc <> 0.
MESSAGE ID SY-MSGID TYPE SY-MSGTY NUMBER SY-MSGNO
        WITH SY-MSGV1 SY-MSGV2 SY-MSGV3 SY-MSGV4.
ENDIF.
READ TABLE i_dynpread INTO w_dynpread INDEX 1.
IF sy-subrc IS INITIAL.
    SELECT land1 FROM t001w
      INTO TABLE i_site
      WHERE werks EQ w_dynpread-fieldvalue.
    IF i_site[] IS NOT INITIAL.
      DATA: lv_line TYPE i.
      CLEAR lv_line.
      DESCRIBE TABLE i_site LINES lv_line.
      IF lv_line GT 1.
        CALL FUNCTION 'F4IF_INT_TABLE_VALUE_REQUEST'
          EXPORTING
            retfield        = 'ZSITEDCDATA-SITE_COUNTRY'
            dynpprog        = 'SAPLZSITEDCDATA'
            dynpnr          = sy-dynnr
            window_title    = 'Site Country'
            value_org       = 'S'
          TABLES
            value_tab       = i_site[]
            return_tab      = i_return
          EXCEPTIONS
            parameter_error = 1
            no_values_found = 2
            OTHERS          = 3.
        IF sy-subrc <> 0.
          MESSAGE ID sy-msgid TYPE sy-msgty NUMBER sy-msgno
                  WITH sy-msgv1 sy-msgv2 sy-msgv3 sy-msgv4.
        ELSE.
          READ TABLE i_return INTO w_return INDEX 1.
          IF sy-subrc IS INITIAL.
            zsitedcdata-site_country = w_return-fieldval.
          ENDIF.
        ENDIF.
Thanks ,
Gaurav

Problem with populating a fact table from dimension tables

my aim is there are 5 dimensional tables that are created
Student->s_id primary key,upn(unique pupil no),name
Grade->g_id primary key,grade,exam_level,values
Subject->sb_id primary key,subjectid,subname
School->sc_id primary key,schoolno,school_name
year->y_id primary key,year(like 2008)
s_id,g_id,sb_id,sc_id,y_id are sequences
select * from student;
S_ID UPN FNAME COMMONNAME GENDER DOB
==============================
9062 1027 MELISSA ANNE       f 13-OCT-81
9000 rows selected
select * from grade;
      G_ID GRADE      E_LEVEL         VALUE
        73 A          a                 120
        74 B          a                 100
        75 C          a                  80
        76 D          a                  60
        77 E          a                  40
        78 F          a                  20
        79 U          a                   0
        80 X          a                   0
18 rows selectedThese are basically the dimensional views
Now according to the specification given, need to create a fact table as facts_table which contains all the dim tables primary keys as foreign keys in it.
The problem is when i say,I am going to consider a smaller example than the actual no of dimension tables 5 lets say there are 2 dim tables student,grade with s_id,g_id as p key.
create materialized view facts_table(s_id,g_id)
as
select s.s_id,g.g_id
from   (select distinct s_id from student)s
,         (select distinct g_id from grade)gThis results in massive duplication as there is no join between the two tables.But basically there are no common things between the two tables to join,how to solve it?
Consider it when i do it for 5 tables the amount of duplication being involved, thats why there is not enough tablespace.
I was hoping if there is no other way then create a fact table with just one column initially
create materialized view facts_table(s_id)
as
select s_id
from student;then
alter materialized view facts_table add column g_id number;Then populate this g_id column by fetching all the g_id values from the grade table using some sort of loop even though we should not use pl/sql i dont know if this works?
Any suggestions.

Basically your quite right to say that without any logical common columns between the dimension tables it will produce results that every student studied every sibject and got every grade and its very rubbish,
I am confused at to whether the dimension tables can contain duplicated columns i.e column like upn(unique pupil no) i will also copy in another table so that when writing queries a join can be placed. i dont know whether thats right
These are the required queries from the star schema
Design a conformed star schema which will support the following queries:
a. For each year give the actual number of students entered for at A-level in the whole country / in each LEA / in each school.
b. For each A-level subject, and for each year, give the percentage of students who gained each grade.
c. For the most recent 3 years, show the 5 most popular A-level subjects in that year over the whole country (measure popularity as the number of entries for that subject as a percentage of the total number of exam entries).
I written the queries earlier based on dimesnion tables which were highly duplicated they were like
student
=======
upn
school
school
======
school(this column substr gives lea,school and the whole is country)
id(id of school)
student_group
=============
upn(unique pupil no)
gid(group id)
grade
year_col
========
year
sid(subject id)
gid(group id)
exam_level
id(school id)
grades_list
===========
exam_level
grade
value
subject
========
sid
subject
compulsory
These were the dimension table si created earlier and as you can see many columns are duplicated in other tables like upn and this structure effectively gets the data out of the schema as there are common column upon which we can link
But a collegue suggested that these dimension tables are wrong and they should not be this way and should not contain dupliated columns.
select      distinct count(s.upn) as st_count
,     y.year
,     c.sn
from      student_info s
,     student_group sg
,     year_col y
,     subject sb
,     grades_list g
,     country c
where      s.upn=sg.upn
and     sb.sid=y.sid
and     sg.gid=y.gid
and     c.id=y.id
and     c.id=s.school
and      y.exam_lev=g.exam_level
and      g.exam_level='a'
group by y.year,c.sn
order by y.year;This is the code for the 1st query
I am confused now which structure is right.Are my earlier dimension tables which i am describing here or the new dimension tables which i explained above are right.
If what i am describing now is right i mean the dimension tables and the columns are allright then i just need to create a fact table with foreign keys of all the dimension tables.

How to know the timestamp of the data in a BI dimension table?

We have some BI dimension tables that we want to know the entry date of each record.
Could you please tell us how to do it?
Thanks a lot!

Hi Jessica,
Is it related to dms? if yes, then please check the field status log where you will get all the information of time and date for DIR's.
which you can use in BI reports.
I hope this will resolve the query.
Regards,
Ravindra

Dimension table and fact table exists data physically

Hi experts,
can anyone plz tell me weather dimension table and fact table exists data physically or not/

Hi..Sudheer
SAPu2019s BW is based on "Enhanced Star schema" or "Info Cubes" database design.This database design has a central database table, known as u2018Fact Tableu2019 which is surrounded by associated dimension tables.
Fact table is surrounded by dimensional tables. Fact table is usually very large, that means it contains
millions to billions of records.
These dimension tables doesn't contain data it contain references to the pointer tables that point to the master data tables which in turn contain Master data objects such as customer, material and destination country stored in BW as Info objects. An InfoObjects can contain single field definitions such as transaction data or complex Customer Master Data that hold attributes, hierarchy and customer texts that are stored in their own tables.
SID is surrogate ID generated by the system. The SID tables are created when we create a master data IO. In SAP BW star schema, the distinction is made between two self contained areas: Infocube & master data tables/SID tables.
The master data doesn't reside in the satr schema but resides in separate tables which are shared across all the star schemas in SAP BW. A numer ID is generated which connects the dimension tables of the infocube to that of the master data tables.
The dimension tables contain the dim ID and SID of a particular IO. Using this SID the attributes and texts of an master data Io is accessed.
The SID table is connected to the associated master data tables via teh char key.
Fact table(Transaction data,DIM ID)<>Dimention Table(SID and Dim ID)<->Masterdata table(SID,IO)
Thanks,
Abha

Query data being picked from which data targets and dimension tables.

Hi Guys,
I need help from you people.
My query is "If we execute any query, i want to know from which data targets and from which dimension tables data being read in the run time", is there any program or any table to find this data.
thanks in advacne.
Regards
Prasad

Hi Prasad,
We will get Data target information in query level in information TAB.
If you want get dimension tables information also you need use technical business content(bwstatistics) Cubes and need to customize the required information. I think standard statisics cubes is not provide dimention tables information. Need to customize that.
Hope it will help for you.
Thanks,
Chandra

Dimension Table populating data

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