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Data Migration
Data migration is the process of selecting, preparing, extracting, and transforming data and permanently transferring it from one computer storage system to another.
Data migration is a common IT activity. However, data assets may exist in many different states and locations, which makes some migration projects more complex and technically challenging than others. Examples of data assets include:
- Unorganized assortments of files stored across many different devices.
- Applications, operating systems, and environments.
- Relational databases like SQL Server, MySQL, PostgreSQL, and MariaDB.
- Unstructured databases such as MongoDB, Azure Cosmos DB, DocumentDB, Cassandra, Couchbase.
- Data lakes, data blobs, and entire datacenters.
- As a result, data migration projects require planning, Data-Migration, and Post Data-Migration & Data Synchronization to ensure their success.
Data Migration stages
The data, applications, etc., will be migrated are selected based on business, project, and technical requirements and dependencies. Hardware and bandwidth requirements are analyzed. Feasible migration and back-out scenarios are developed, as well as the associated tests, automation scripts, mappings, and procedures. Data cleansing and transformation requirements are also gauged for data formats to improve data quality and to eliminate redundant or obsolete information. Migration architecture is decided on and developed, any necessary software licenses are obtained, and change management processes are started.
The planning phase accounts for all preparations made before the migration process. The planning phase consists of;
- Evaluation of data source and target systems
- Solution design
- Plan budgets Building and Testing
- Building and Testing
- Data Backup
This phase includes the extraction and loading of data (the E and L of ETL). Depending on the chosen migration process and volume of data, migration may occur in several days or over several phases. Businesses should consider the need for availability of their services when choosing an approach as a big-bang approach will not be ideal for applications needing real-time availability.
Monitoring and auditing also form a critical part of the migration phase as the process needs to be examined and monitored to ensure the accuracy of the entire process. Auditing ensures data migration proceeds according to set guidelines, and the final migrated data is of excellent quality for business use. Frequent testing and monitoring should occur throughout the implementation process to ensure the safe transit of data.
Hardware and software requirements are validated, and migration procedures are customized as necessary. Some sort of pre-validation testing may also occur to ensure requirements and customized settings function as expected. If all is deemed well, migration begins, including the primary acts of data extraction, where data is read from the old system, and data loading, where data is written to the new system. Additional verification steps ensure the developed migration plan was enacted in full.
After data migration, results are subjected to data verification to determine whether data was accurately translated, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss. Additional documentation and reporting of the migration project is conducted, and once the migration is validated complete, legacy systems may also be decommissioned. Migration close-out meetings will officially end the migration process.
Verifying the migrated data’s accuracy and completeness takes place in this phase by running the source and destination systems parallel to each other to observe and verify their functionality in the new system. A sidestep from the intended functionality could pinpoint a variety of reasons which may need further investigation. This continuous verification after the migration is also considered best practice for efficient migration processes.
Data synchronization ensures accurate, secure, compliant data and successful team and customer experiences. It assures congruence between each source of data and its different endpoints. As data comes in, it is cleaned, checked for errors, duplication, and consistency before being put to use. Local synchronization involves devices and computers that are next to each other, while remote synchronization takes place over a mobile network.
Data must always be consistent throughout the data record. If data is modified in any way, changes must upgrade through every system in real-time to avoid mistakes, prevent privacy breaches, and ensure that the most up-to-date data is the only information available. Data synchronization ensures that all records are consistent, all the time.
- Keeping data secure.
- Maintaining data quality.
- Quality Data Management.
- Data harmonization.