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TCS Confidential

Best Practice

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26 February 2010

SAS JOB

IMPROVEMENTS

DC/ SC/ Geo/Practice: KOLKATA

Name of the author: RAHUL SHARMA

EMAIL

rahul28.s@tcs.com

Date Created: 17

February

2010.

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Description

Project / ContextOur Customer is United Kingdoms largest supplier of retail creditservices. It has collaboration with number of banks, operatingwith a number of financial products for the consumer marketwithin the UK.In SAS environment multiple jobs are running which are basicallydoing some reporting and DATAMANAGEMENT tasks. The jobsuse files which are created by teams like Cardpac, VisionPLUS,Equifax, etc.

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How we did it

Problem Scenario

XXX job on SAS Server uses files sent by Mainframe Teams. If any of the

files does not arrive by the end of the day, then the job fails with the reason

that File not Arrived .

If few of the files from the complete set of files had already arrived to the

SAS Server and then the job failed, then these files stays there unutilized.

On next day when the files for the job for that day arrives, then previous

days files which are already lying there at the SAS Server gets overwritten

with the current one. Getting these files back from the Mainframe Teams

involves a lot of manual work. This manual effort ranges between 8 to 16

hours.

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How we did it

Highlights

This helped us saving time ( in the range of 8 to 16 hours) and also thenumber of incidents raised with the SAS Support Team.

Process adopted

This was resolved by splitting up the XXX job into smaller sections so that

each job works on respective files and does not wait for the complete set of

files to arrive. Like this the files which arrive at the SAS Server gets picked

up by its respective job and the necessary update takes place. Hence the

issue of over-writing of the file due to job failure was resolved.

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How we did it

Problem Scenario

XXX job on SAS Server pulls data from an Online DATABASE. The

amount of data pulled is huge (on weekly basis).

It is of the range of 2.5 to 3.0 gigabytes. The original job had four

steps of pulling data. The database from which data had to be

pulled, was not available after 9:00 pm BST. In one single day this

job was not getting completed because each data pulling step

used to take something around 9 hours.

As a result of this every week the job used to fail and then had to

be fixed manually. The manual fix used to take at least two or more

days to complete.

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How we did it

Highlights

This helped us reducing the number of failure count to the level of 95%reduction and also the number of incidents raised has gone down by99% with the SAS Support Team. It also saved the time as the manualwork was removed.

Process adopted

To resolve this issue, the job was broken down and five new jobs

were coded. Four of the jobs had the step to pull the data from the

database and the fifth one had the step of combining these

datasets together. In this manner the four data pulls were

scheduled on four separate days and the final job was scheduled

on the fifth day.

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Why this is a best practice

Benefits1.Manual work on the part of SAS Team reduced.2. Less number of batch failures3. Reduced number of Online Incidents4. Productivity improved through batch optimization. The jobs finished in

lesser amount of time in comparison to earlier times.5. Customer satisfaction.

The actual beneficiary is Customer and customer has got the benefitpermanently.

Justification

These are best practices because all the processes described in theprevious slides proved to make the jobs run in a better way thus making

the BATCH more stable. These also made the jobs error / abend free andhelped us meet the SLA by optimizing the CPU utilization. This alsohelped in terms of potential cost benefit to Customer.

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How this may be adapted elsewhere

Contact Info

Kakali Datta (+913366369361)Rahul Sharma(+913366369362)

Replication

The SAS production Support kind of projects across the TCS would

get ideas for Batch tuning/System optimization by going through theprocesses that were adopted for XXX JOB (SAS Production Support)project.

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THANK YOU