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The differences between database migration and modernization


Database migration vs. modernization is an example of the classic investment timing tradeoff.  Database migration looks quick, but can miss huge opportunities for cloud cost optimization. Data modernization looks difficult, but management of the existing legacy infrastructure relocated to the cloud could prove unfeasible. What’s the right approach?

Database migration is usually rehosting 

In a 2017 survey of 1400 IT professionals by McAfee, users identified two main tactics for moving data to the cloud: database migration and database modernization.  Database migration is rehosting existing on-premise databases to a cloud host. Database migration usually restricts the target to the same software stack; some changes in software version or configuration will depend on the migration tool. 

Clearly, data migration cannot be the principle cloud strategy going forward. This is true because legacy databases and applications are a huge drain on time and resources for most organizations.  As they age, they simply cannot be maintained in their current form.  According to Oracle, the average company spends 60-85% of its IT budget maintaining legacy applications that fail to meet the changing competitive needs of the business.

Modernization is the big payoff

Database modernization is the process of evolving legacy database technologies to a mix of open, standards–based datastores. This mix usually includes structured and unstructured databases which each provide specific needs of the application.  The open source database may be self or cloud-provider managed.  

While modernization may distribute data to application-specific datastores, it must not damage data quality.  Any meaningful data relationships or context that existed in the legacy datastore must be preserved.  This preservation can be quite challenging in more ambitious modernizations, such as the introduction of microservices database architecture

The potential cost savings alone are compelling. According to Gartner, organizations can reduce their database spend by 80% or more with a database migration process to an open source database such as EDB Postgres in place of Oracle.  Given the huge number of applications needing attention, data modernization has the potential to dramatically reshape IT cost structures in years to come.  

Logical data migration optimizes both database migration and modernization

Griddable operates at the data layer, so it seamlessly migrates across heterogeneous clouds and database types.  The Griddable data pipeline preserves the sequence of each transaction as it occurs. In addition, the Griddable policy engine provides user-friendly controls for copying and transforming data in transit. Griddable masks or encrypts any number of individual data elements using separate masking algorithms or encryption keys.  It also filters and replaces data values, or selectively removes entire rows or columns, with an easily-defined user policy.

Griddable also optimizes simple database migration with automated, integrated initial load operation.  Using Griddable, database switchover in production occurs in near-zero downtime.  DBAs and architects are free to choose the time of switchover to fit with other interdependent activities.

Database modernization usually requires migration to multiple targets simultaneously. The Griddable transaction grid copies source data to multiple subscribing destinations at once, with high efficiency.  While copying data, Griddable provides complete control of data customization to each target.  Further, Griddable rearchitects source schemas in a monolithic database into multiple database targets which each contain the relevant data.

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