IBM InfoSphere Information Server vs. pandas

Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

Pandas are great for quick and relatively simple analytics and visualizations

Pandas work well for exploratory ad-hoc analytic work

But , We had little success in implementing complicated predictive analytics. And large data sizes can be a problem.

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• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

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• It is easy to do statistical analysis
• It is easy to clean the data
• It is easy to produce graphs and charts to visualize

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• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

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• There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
• Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
• In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case

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• Scale of implementation
• IBM techsupport

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Over the years, we tried a lot of different frameworks and tools, homegrown and commercial. Pandas provide the best results.

It is lightweight, flexible and easy to implement.

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DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.

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• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.

• Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
• Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers

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