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Is moving data to the cloud as easy as it sounds? Not really,

because when you want to move huge volumes of data to the cloud, it can well turn out to be a daunting task. More important than the sheer volume of data which must be migrated is the risks associated with how these applications that have been moved will behave eventually in the cloud.

What is the reason behind adopting the cloud?

Cloud data migration is expected to help businesses cut down on costs but no one can deny the fact that a lot of time will be needed to shift all the data. The collection, organization and formatting of data is a challenging task and when you decide to sign up for cloud migration, you should consider all the key bottlenecks that will come your way.

What are the challenges one can face in cloud data migration?

Issues arising out of data storage

One of the biggest problems businesses face is that of data storage. In cloud migration, data is often pushed into the new environment without even considering how this data will then be used. While storing data and documents in the cloud seem to be cheaper, these files and databases may act quite differently inside the cloud. Files are usually organized according to a hierarchy and every file may be accessed with least latency and very high speed. While file storage is found to be most cost-effective in the cloud it has a few drawbacks. In order to get high performance most of the cloud-based files may be accessed at one time by a single virtual machine. So, all applications that need that data can run on a single VM alone; to have many virtual machines means you need a NAS or Network Attached Storage that can affect performance. While file systems are flexible and fast, they are costly and only useful for applications that run in the cloud.
Overlooking budget planning before cloud data migration

Shifting data into the cloud is not the same as copying the bytes into a particular storage type. You need to undergo extensive preparations before anything may be copied. This needs careful budgeting on its part. There is proof-of-concept projects that tend to overlook this and it leads to expensive overruns eventually. You may save costs and time if you filter unwanted data. This helps you prioritize the data which must be moved first. So, data which is being used actively cannot be kept out of sync for a long time or for as long as the cloud migration is not over. What is therefore needed is an automated solution to pick up data which must be shifted and when. Moreover different workflows will need data in different formats. For instance, a legal task may need translating many small Word documents and converting these into zip files. A media workflow, on the other hand, will need transcoding while a bioinformatics workflow will need identifying genomics data. This reformatting is a tedious and time-consuming task. A solution for this is compressing and archiving the data which makes it easy to shift it and store it. However, you should also take into account the space and time this will take up.
Data Corruption Instances

Checking data integrity is a key step in cloud migration that is also the most prone to error. It is usually believed that corruptions take place when data is in transit and this can be detected by checksums. While checksums are integral to the process, it is primarily the way data is prepared and imported during which data corruption and loss can actually occur. Simple incompatibility in software versions for instance can make “correct” data completely useless.

Lack of Scalability

When you are trying to lift one system into the cloud it is easier to simply copy the prepared data into physical media. However, this process is hard to scale and what appears to be rather easy often balloons into a nightmare when there are different systems at play. You will need to install software, update drivers and juggle connectors to get this done and the software installation and copying speed can drop substantially. Shifting the data from every machine across the Internet is perhaps simpler when data is ready for the cloud. So, when data involves exporting, copying, archiving etc local storage is an issue; you will need dedicated storage.
Compatibility Problems Post Migration

When data reaches the destination cloud also, the migration process is not over. You will need checksums to ensure that all the bytes which have reached match those which had been sent. This is not as easy as you think because file storage uses many layers of caches which can hide data corruption. While this data corruption is a rarity, you cannot be sure of checksums until all the caches have been cleared and the files re-read. When the data which is transferred has been verified you may need to perform more extraction and reformatting before the cloud applications can use it.

So, cloud migration has to do more with processes than with data. Even the so-called apparently simple tasks such as file distribution may need complicated migration steps to make sure that the cloud architecture matches the workflow desired. While the hype around cloud scalability is justified, you need careful planning and awareness of possible bottlenecks before you can enjoy the returns. So, without further delay, you should sign up with a reputed cloud provider like CloudOYE to get on with cloud migration minus these bottlenecks.

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