The most popular approach to database migration is known as "extract, transform, load" (ETL) technique. Extraction phase is where schemas, constraints and data are extracted from the original database; transformation is where all extracted objects are transformed for storing in the proper format; loading phase where schemas, constraints and data are loaded into the final target database.
The first part of an ETL process involves extracting all database objects from the source system. In many cases, this represents the most important aspect of ETL, since extracting database objects correctly sets the stage for the success of subsequent parts of migration process. The most appropriate formats for intermediate schema export are XML and JSON. For purpose of temporary storage for data the comma separated values format is the best choice. For complicated databases the extraction process must include validation rules in order to pass correct database objects to the next phase of migration process.
Transformation stage applies number of rules to the extracted database objects in order to prepare them for loading into the target database. The most important functions of transformation are mapping types that do not have direct equivalent, cleaning data in order to make it complied with destination rules, handle character sets that are available in source system but may not be in the target.
Below are examples of transformation that may be required while migrating database between different systems:
Finally, the load phase imports schemas, constraints and data to the destination database. Depending on the requirements it may overwrite or update existing data that is frequently done on a daily, weekly, or monthly basis. In case of update existing tables, it is necessary to disable constraints and triggers in order to preserve data integrity and increase performance of ETL process. More complex systems may also require history and audit trail of all changes being made to the database.
Utilizing ETL tools for database migration profiling brings several benefits, streamlining the process and optimizing outcomes. These advantages include:
Insufficient quality of the data can be a significant challenge for database migration projects. ETL tools can help to recognize data quality issues by applying miscellaneous validations:
Some of ETL tools can check data quality issues but cannot resolve them, others can fix those issues automatically. It is extremely important to properly address data quality issues before they can impact the success of the database migration project.
By leveraging ETL tools for database migration profiling, organizations can achieve a streamlined and controlled process, reduce manual effort and errors, enhance data quality, ensure data visibility and governance, and mitigate risks associated with the migration.
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