It's a late response, but I believe this information will be quite handy for anyone seeking a similar solution.

It's a complex process that requires a delicate balance of understanding the old, embracing the new, and being mindful of the potential pitfalls along the way. One of the biggest challenges is ensuring that your valuable, time-tested data and processes can make the leap without getting lost or compromised.

Imagine you're trying to teach your grandma to use Instagram. She's got all these amazing stories (data) that you know people would love, but you've got to translate them into a format that fits the platform, right? It's a bit like that. You've got this treasure trove of information locked away in systems that were cutting-edge when disco was still a thing. Now, you want to bring it into the cloud era, where it can drive innovation and decision-making.

The trick is to do it without causing a data apocalypse. This involves a lot of planning, a deep understanding of both your old and new systems, and a strategy that often includes middleware, data transformation, and a healthy dose of patience.

For those embarking on this journey, especially when considering the leap from mainframe to cloud, it's crucial to be aware of the integration risks involved. There's a fantastic blog post over at Kumaran.com that dives deep into these challenges.

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