It performs on dataset read into memory from any other parallel job stage that can
output data.
The main uses of the lookup stage is to map short codes in the input dataset onto
expanded information from a look up table which is then joined to the data coming from
input. For example, some we get the data with customers name and address. Here the
data identifies state as a two letters or three letters like mel for melbourne or
syd for sydney. But you want the data to carry the full name of the state by
defining the code as the key column. In this case lookup stage used very much.
It will reads each line, it uses the key to look up the stage in the lookup table.
It adds the state to the new column defined for the output link.
So that full state name is added to the each row based on codes given.
If the code not found in the lookup table, record will be rejected.
Lookup stage also performs to validate the row.
Look Up stage is a processing stage which performs horizontal combining.
Lookup stage Supports
N-Inputs ( For Norman Lookup )
2 Inputs ( For Sparse Lookup)
1 output
And 1 Reject link
Up to Datastage 7 Version We have only 2 Types of LookUps
a) Normal Lookup and b) Sparse Lookup
But in Datastage 8 Version, enhancements has been take place. They are
c) Range Look Up And d) Case less Look up
Normal Lookup:-- In Normal Look, all the reference records are copied to the memory and the primary records are cross verified with the reference records.
Sparse Lookup:--In Sparse lookup stage, each primary records are sent to the Source and cross verified with the reference records.
Here , we use sparse lookup when the data coming have memory sufficiency
and the primary records is relatively smaller than reference date we go for this sparse lookup.
Range LookUp:--- Range Lookup is going to perform the range checking on selected columns.
For Example: -- If we want to check the range of salary, in order to find the grades of the employee than we can use the range lookup.
output data.
The main uses of the lookup stage is to map short codes in the input dataset onto
expanded information from a look up table which is then joined to the data coming from
input. For example, some we get the data with customers name and address. Here the
data identifies state as a two letters or three letters like mel for melbourne or
syd for sydney. But you want the data to carry the full name of the state by
defining the code as the key column. In this case lookup stage used very much.
It will reads each line, it uses the key to look up the stage in the lookup table.
It adds the state to the new column defined for the output link.
So that full state name is added to the each row based on codes given.
If the code not found in the lookup table, record will be rejected.
Lookup stage also performs to validate the row.
Look Up stage is a processing stage which performs horizontal combining.
Lookup stage Supports
N-Inputs ( For Norman Lookup )
2 Inputs ( For Sparse Lookup)
1 output
And 1 Reject link
Up to Datastage 7 Version We have only 2 Types of LookUps
a) Normal Lookup and b) Sparse Lookup
But in Datastage 8 Version, enhancements has been take place. They are
c) Range Look Up And d) Case less Look up
Normal Lookup:-- In Normal Look, all the reference records are copied to the memory and the primary records are cross verified with the reference records.
Sparse Lookup:--In Sparse lookup stage, each primary records are sent to the Source and cross verified with the reference records.
Here , we use sparse lookup when the data coming have memory sufficiency
and the primary records is relatively smaller than reference date we go for this sparse lookup.
Range LookUp:--- Range Lookup is going to perform the range checking on selected columns.
For Example: -- If we want to check the range of salary, in order to find the grades of the employee than we can use the range lookup.
Lookup stage is best processing stage which performs horizontal Combining in the better way.
ReplyDeleteLook Up Stage
what is the case less look up stage
ReplyDelete