Datastage general overview.
Use Cases and Deployment Scope
It handles large business scale data. Since this an example of ETL software, it can handle large amount of data records especially banking information to our clients, migrating and generating them into new sets of information that we can utilize into a better tangible data for processing. It can generate alot of information based on a lot data dump that we can utilize for more business refined approach.
Pros
- Executing batch jobs (datastage jobs).
- Compiles a lot of datasets into a new set of information.
- It creates more refined information based on what we create and define from the job logics created.
Cons
- Parameter refreshes the variables that need to be refreshed all the time when a new job is deployed.
- Defining variables one by one takes a lot of time especially those are hard coded.
- Jobs are sometimes get hung.
- Sessions locks up the jobs.
Likelihood to Recommend
DataStage is somewhat outdated for an ETL. I guess that's what makes it a bit lagged behind its competitors. It can be used for data processing, sure, but its performance seems to be lagging behind or quite slow given the server it is running from. I won’t depend on this application if it's handling a lot of mission-critical banking and business data.
