· What are some good SAS programming practices for
processing very large data sets?
Answers were Sorted based on User's Feedback
Answer / sibin
2.DROP KEEP in Set Statement
4.Mutually Exclusive Conditions like DO ,DO UNTIL,DO WHILE
|Is This Answer Correct ?||16 Yes||1 No|
Answer / pricil kurian
1) Compressing dataset
2) Using where instead of if as subsetting conditions
3) Deleting the temporary datasets at the end of the program
4) If we use "if..else ..if conditions"..use most occuring
5)Use keep and drop appropriately to avoid the unneceesary
6).Creattion of index variable
7)Use of array statement
8)Create one byte required numeric variable which we don't
use for calcualting process as charcater..Using this this
variable need 1 byte where numeric variable's minimum
length is 3.
9)Minimize the CPU time that SAS uses by executing the
minimum number of statements in the most efficient
10)Use SQL for code simplification. A single SQL statement
is generally easier to code and understand than a
long series of SORT and DATA steps.
11)Use of data _null_; when we don't need to output into a
12)Use macros for redundant code.
13)When data sets are large and unsorted, the SQL inner
join may beter than SORT and MERGE.
|Is This Answer Correct ?||8 Yes||0 No|
Answer / koduri
Using Arrays .
|Is This Answer Correct ?||3 Yes||1 No|
Answer / vicky
1.we can compress the dataset
2.if we wana do merge a bigdate set with another dataset
having few variables for instance a fact with dimension
having key variable and description column
in this case sorting n merging this bigdateset(fact) with
dimension apply formats.so dat u can avoid sort n merge.
|Is This Answer Correct ?||2 Yes||1 No|
Answer / dvsr
1)compress the datasets
2)use DROP KEEP options while creating temp data sets
3)Index creation on the Frequently using variables
|Is This Answer Correct ?||0 Yes||1 No|
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