The process of data migration practically refers to moving data from one place to the other or converting it from one format to another format. There are several reasons why organizations decide to conduct data migration to protect important and sensitive information. The most common reasons include upgrading a particular database or application, establishing a new data warehouse, or mixing new data with the older version to improve the quality of it.
Although the entire procedure may seem easy in an apparent manner, it takes a lot of effort and precautions to conduct a successful data conversion with no error whatsoever. Data gravity makes it difficult at times for organizations to conduct successful data migrations. There are data migration challenges that prevent its way, which include but are not limited to data corruption risk, extended downtime risk, application stability risk, semantics risk and data loss risk.
If the target system does not work properly for some defective settings, the data migration process would not be successful, and it may eventually lead to data corruption.
A properly planned Data migration strategy is extremely important as far as conducting successful data conversion is concerned. While conducting data migration, its order must be correct, and a chronological order should be maintained. Otherwise, the risk of orchestration emerges. Orchestration may further lead to interference risks.
There are several ways of conducting data conversion. Some of the major types of data migration are as follows:
In order to conduct a proper data conversion, certain data migration steps should be taken into account by the organizations. The entire data migration process consists of three simple steps: Extracting data, transforming it and finally restring it in the target system.
Some of the most effective data migration techniques include proper planning the conduction of the process with data migration tools beforehand; evaluating the nature of the data properly. Landscape analysis is another major step. Data migration testing is an essential step that should not be avoided to conduct a successful conversion.
Properly executing the transferring data is important. After the whole process, the removal of the old version is of utmost importance.
Some of the major data migration challenges that often interrupt the data conversion process are listed below:
These are some of the major data migration risks that are often seen to take place.
These terms are interchangeably used frequently. Although the nature of all these procedures seems to be similar, there is a huge difference in nature that makes each one of these different from each other. Data migration refers to the process of data cleaning, filing, transferring, and it can refer to location changes and format changes as well. Whereas data conversion mainly refers to transferring data from one format to another, i.e. a partial process that falls under the data migration process.
However, data integration refers to the process of merging one data-information to another. Therefore, it refers to the process of combining data from different sources to provide unified data to users.
The most convenient options of moving to infrastructure as a service (IaaS) would be re-hosting, modifying and extending application code and finally replacing the older version. To move to a platform as a PaaS service, running the application over the cloud and then discarding code should be enough.
Data migration is a complex procedure that should be done with utmost guidance, protection and supervision. One tiny step may lead to big negative repercussions, including unsuccessful conversions. Therefore, pre-planning and evaluating the data migration process before conducting it of utmost importance.
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