what is the best stage in datastage parller jobs to use full
out join and why
Answer Posted / rajesh
join stage
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create a job that splits the data in the Jobs.txt file into
four output files. You will direct the data to the
different output files using constraints. • Job name:
JobLevels
• Source file: Jobs.txt
• Target file 1: LowLevelJobs.txt
− min_lvl between 0 and 25 inclusive.
− Same column types and headings as Jobs.txt.
− Include column names in the first line of the output file.
− Job description column should be preceded by the
string “Job
Title:” and embedded within square brackets. For example, if
the job description is “Designer”, the derived value
is: “Job
Title: [Designer]”.
• Target file 2: MidLevelJobs.txt
− min_lvl between 26 and 100 inclusive.
− Same format and derivations as Target file 1.
• Target file 3: HighLevelJobs.txt
− min_lvl between 101 and 500 inclusive.
− Same format and derivations as Target file 1.
• Rejects file: JobRejects.txt
− min_lvl is out of range, i.e., below 0 or above 500.
− This file has only two columns: job_id and reject_desc.
− reject_desc is a variable-length text field, maximum
length
100. It should contain a string of the form: “Level out of
range:
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