In order to have successful cloud integration, you need to be smarter in your approach. You need to avoid these silly mistakes when you integrate your data between your data center and the cloud. As data and applications grow in number, it becomes more necessary for you to synchronize them. Many use EDI technologies, but others still employ USB drives – which is a mess! You should know better. Here are 3 cloud integration mistakes that you should avoid.
- Not having error-handling routines. As you know, data integration may fail. Plus the fact that you are linking systems on public cloud providers with enterprise data centers makes the likelihood of failure greater. Your inability to deal with error-handling issues will cause problems with the quality and availability of data – something you should manually correct. Thus, you need to consider error-handling routines in your integration tools and not just on-premises.
- Not defining the metadata and the true meaning of the data. Not being able to define the metadata and the true meaning of the data makes it difficult to determine the best approach to data exchange with systems hosted in local data centers and the public clouds. When outlining a data integration strategy, it is important to know what the meaning of the data is and how it should be managed.
- Not understanding bandwidth limitations. It is fine to transfer data over an open internet, only if it’s a few megabytes. However, when you deal with gigabytes of data, latency and bandwidth costs become greater. This is one of the cloud integration mistakes you should avoid as big data systems are built on public clouds while operational data remains in the enterprise.
If you encounter these issues in your cloud integration approach, do not panic. Just take a step back and plan again. Rethink the technology you are using, run a test and implement again. Spend a few dollars and invest in a few weeks. It won’t be long until you see a difference. Enterprises consistently make mistakes on these three points. As what I’ve said before: You should know better.