Database migration is the transferring of data between storagetypes, formats, or computer systems. Database migration isusually performed programmatically to achieve an automatedmigration, freeing up human resources from tedious tasks. It isrequired when organizations or individuals change computersystems or upgrade to new systems. For example:- Organisation want to move from onedatabase to other because of some business requirement, thenthey can go for database migration. Capital One is one of the USlargest credit card industry, initially they used oracle as theirdatabase, but when their volume grow Oracle not able tosupport this huge volume of data, then they move to anotherdatabase called as Teradata which can store terabyte ofinformation and improve the data processing speed.
Pre Migration :- Analysing, Mapping, Design Migration : - Transform, Normalize and backup Post Migration : Quality Control, Clean-up, Maintenance.
Analysis:- The analysis phase of data migration should be scheduled to occur concurrently with the analysis phase of the core project. The aim of the analysis phase in data migration projects is to identify the data sources that must be transported into the new system. For example what is my source database and what is my target database. Mapping:- Mapped each source field to target field. Eg. In my source table I have emp_id, salary as column in target database what should be my column name and its datatype. Design :- After you have decided upon the legacy data sources and have conducted thorough data analysis, you must begin the roster selection. This involves going through the list of data elements from each and every source data structure, and deciding whether to migrate each one.
Transform:- After the design phase Transform phase start. In this phase data transform happen from source to target system. For this organization can use different ETL(Extract, Transform, Load) process. ETL Process Source Target
Normalize:- After moving the data to Target system, data normalization required. Such as remove inconsistence data, remove duplicate data, storing data based on Normal form. Also moving data from stage layer to Target layer. Normalization Stage Stage Process System System
Back Up:- This is one of the important phase of the migration. After transforming data from source to target state organization need to create backup process. This will help during database maintenance and recovery process.
Quality Control:-This is the import phase in Post migration process. Here testing team need to check target database table structure, column type and its value. Also need to map this value with source system value. If any error occur then raise an defect and assign to the concern team. Also ask Business to verify their system whether the output is as expected or not. Also need to create reports, wellness check document and published these document in the organization portal. Clean-Up:- During migration process data transformation happened through different channels, such as file system, Temporary tables etc. After the migration process complete need to clean these temporary system. Maintenance:- The maintain phase is where all of the mappings are validated and successfully implemented in a series of scripts that have been thoroughly tested. In all organization they have separate team which take care of this process, they maintain the Database, do housekeeping. Organization spend huge amount in the maintenance phase.
The solution which is proposed here works on the concept of ETL methodology where in the data is extracted from the source database transformed within staging database and then commenced into target database. Whether to use staging database or not depends on the fact that what is the amount of cleansing and data transformation is required. Minimal amount of data cleansing or transformation can be managed within the data migration tool using a query builder tool but as a best practice major cleansing has to be done using a staging database.