What are Slowly Changing Dimensions?
Slowly Changing Dimensions (SCD) - dimensions that change slowly over time, rather than changing on regular schedule, time-base. In Data Warehouse there is a need to track changes in dimension attributes in order to report historical data. In other words, implementing one of the SCD types should enable users assigning proper dimension's attribute value for given date. Example of such dimensions could be: customer, geography, employee.
- Type 0 - The passive method
- Type 1 - Overwriting the old value
- Type 2 - Creating a new additional record
- Type 3 - Adding a new column
- Type 4 - Using historical table
- Type 6 - Combine approaches of types 1,2,3 (1+2+3=6)
There are many approaches how to deal with SCD. The most popular are:
accel-DS Shell Script Engine V2.0 is Now Availableaccel-DS Shell Script Engine can data transformation using sql,files with most of the popular database and file formats used in Hadoop.
In Type 2 Slowly Changing Dimension, a new record is added to the table to represent the new information. Therefore, both the original and the new record will be present. The new record gets its own primary key.
In our example, recall we originally have the following table:
After Peter moved from Washington to Maine, we add the new information as a new row into the table:
Advantages:This allows us to accurately keep all historical information.
Disadvantages:This will cause the size of the table to grow fast. In cases where the number of rows for the table is very high to start with, storage and performance can become a concern.
This necessarily complicates the ETL process.
Usage:About 50% of the time.
When to use Type 2:Type 2 slowly changing dimension should be used when it is necessary for the data warehouse to track historical changes.
|1||Target_Table||Table where data should be loaded|
|2||Join_Columns||Enter source sql column and target table column, join will be created between source and target using these columns.|
|3||Mapped_Columns||Enter source sql column and target table column, source column data will be loaded in the respective target table column.|
|4||Source_SQL||Enter Source SQL query, which has new and updated records..|
|5||Target_Columns||Enter all the target table column names seperated by comma in the insersion order.|
|6||Eff_Date_Column||Enter target table column name where record created date will be loaded. Enter 'NA' if you don't want to track created date.|
|7||End_Date_Column||Enter target table column name where record last updated date will be loaded. Enter 'NA' if you don't want to updated track date.|
|8||Latest_Rec_Column||Enter target table column name where flag will specify the record is latest or old. Enter 'NA' if you don't want to track latest record.|
|9||Data_Base_Name||Hadoop Database Name where the Target Table is located.|
|10||Transpose_Flag||Flag for Transpose. Enter 'Y' if you have Transpose column to load.|
|11||Transpose_Track_Column||Enter the source sql transpose column name, target table transpose column name and source column names to track transpose column (#This option is not applicable if (Arg) Transpose_Flag is 'N')|
|12||Audit_Columns||Audit columns delimited by ',' FORMAT : "creation_ts-CURRENT_TIMESTAMP,created_by-DEFAULT-VISHNU,process_control_id-UNIQUE_VALUE"|
|13||SQL_File_Location||Enter the path in which downloaded Type_2.sql file present.|
|14||Log_File||Log File path and name to log the script execution details.|
Target_Table="$Target_Table$" Source_SQL="$Source_SQL$" Mapped_Columns="$Mapped_Columns$" Join_Columns="$Join_Columns$" Target_Columns="$Target_Columns$" Data_Base_Name="$Data_Base_Name$" SQL_File_Location="$SQL_File_Location$" Log_File="$Log_File$" ./Type_1.sh
Target_Table="EDS_Request_Prod_KV" Join_Columns="ACCOUNT_ID,ACCOUNT_ID,SSN,SSN,SRC_SYSTEM,SRC_SYSTEM,ROW_TYPE,ROW_TYPE" Mapped_Columns="ACCOUNT_ID,ACCOUNT_ID,SSN,SSN,SRC_SYSTEM,SRC_SYSTEM,KV_COLUMN,KV_COLUMN,ROW_TYPE,ROW_TYPE,ADDRESS1,ADDRESS1,ADDRESS2,ADDRESS2,ADDRESS3,ADDRESS3,ADDRESS4,ADDRESS4,CITY,CITY,COUNTRY,COUNTRY,STATE,STATE,POSTAL,POSTAL" Source_SQL="SELECT * FROM cygnus.EDS_Request_STG_KV" Target_Columns="ACCOUNT_ID,SSN,SRC_SYSTEM,KV_COLUMN,ROW_TYPE,ADDRESS1,ADDRESS2,ADDRESS3,ADDRESS4,CITY,COUNTRY,STATE,POSTAL,as_of_date,creation_ts, created_by,process_control_id,effective_date,latestcol" Eff_Date_Column="effective_date" End_Date_Column="as_of_date" Latest_Rec_Column="latestcol" Data_Base_Name="cygnus" Transpose_Flag="Y" Transpose_Track_Column="kv_column,kv_column,ADDRESS1+ADDRESS2+ADDRESS3+ADDRESS4+CITY+COUNTRY+STATE+POSTAL,ADDRESS1+ADDRESS2+ADDRESS3+ADDRESS4+CITY+COUNTRY+STATE+POSTAL" Audit_Columns="creation_ts-CURRENT_TIMESTAMP,created_by-DEFAULT-VISHNU,process_control_id-UNIQUE_VALUE" SQL_File_Location="/home/cloudera/Desktop/Type_2" Log_File="/home/cloudera/Desktop/Type_2/type2_EDS_log.txt" ./Type_2.sh
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