Alooma vs. IBM Cloud Pak for Integration

Very appropriate for managing an ELT stack -- Alooma handles the 'E' and 'L' pieces very well. Due to the way transformation is setup, it is more on the lightweight side of things and thus works for this kind of stack. For more traditional ETL stacks, Alooma would probably not be the right fit.

Incentivized

We are always working on improving our brand - good customer care and efficient services are the roads to that. By picking IBM's AI automation, we are able to coordinate and connect processes around the company to deliver efficient services on store and online on our website. We've been able to seamlessly automate and digitize most of our operations.

Incentivized

• Transformation of data before entering the data warehouse -- allows for a consistent/understandable schema for analytics teams.
• Adding of additional data sources: Alooma has a large library of pre-built, common 3rd party vendors for which data is often needed to be ETL-ed.

Incentivized

• Test generation gives useful insights into missing tests before launching the bots.
• Open API files helps us understand what is running behind the scenes.

Incentivized

• More tools on managing the data warehouse itself. Although great on the ingest and transformation of data, more help provided by the tool on managing the outputs would be welcome.
• Increase possibilities in the transformation step, rather than single event transformations.

Incentivized

• Natural language processing needs more attention to have more flowing conversations.

Incentivized

Haven't used the alternatives, since Alooma was one of the earlier ones to the market. From a cursory viewing, however, the features seem to match relatively similarly.

Incentivized

• Allowing for additional data sources to more automation capability around monitoring the business is a huge ROI

Incentivized

• Achieving consistency across operations.
• IBM RPA provides bots to handle the micron tasks which are monotonous and boring.

Incentivized