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Evaluating Cloud Migration Process Concerning Data and Analytics

By CIOReview | Tuesday, July 14, 2020

More than 60 percent of organizations rely on cloud computing today. Hence, evaluating the influencers, data migration, and governance concerning cloud-based data and analytics is essential.

FREMONT, CA: Today organizations, of any scale, are enthusiastic about adopting Cloud technologies for meeting the challenges of the data dynamicity. Concerning data and analytics, cloud platforms have proved to be very efficient and convenient for organizations. 

This post will highlight the points that one should consider while strategizing cloud migration for their organization in terms of data and analytics.

Key Influencers

While devising the Cloud migration, Cloud providers like Azure, Cloud adoption strategy (whether single or poly cloud platforms), the target state, and implementation approach, majorly influence the process.

Target State and approach

Cloud migration is dependent on defining and constructing the target state. These approaches can be:

Lift and shift, where the cloud platform serves as Infrastructure as a Service. In this case, planning for moving the data to the cloud from on-premise, is enough to achieve the goal.

Amend and shift, where organizations introduce features like Data Lake storage, for optimizing their migration process.

Re-engineer and shift, where a complete transformation takes place. From retrieving data from the source to its distribution, every data platform layer undergoes modification.


Data is the parent component in the practice of cloud computing. In this regard, both Historical as well as Incremental data hold importance. The volume of historical data could vary according to the industries. For the security and migration of historical data, one must keep in mind format conversions, supported target database, the volume required from moving the data to the target, and other related points.

The ongoing incremental data requires accurate timing and seamless flow through several layers from data generators.

Persistent Layer

There needs to exist a suitable layer for preserving the datasets, based on the target state. The migration process should be capable of target partitioning and location mapping for monitoring the locations from where and to the data moves. In most cases, organizations preserve data in Hadoop. Many cloud databases offer conversion services and delineate the areas that require manual redressing.

Reports and visualization

Developing accelerators for conversion, views for generating similar data structures for adapting to the alterations in the data model, and introducing rationalization for avoiding migrating redundancy, are critical considerations.

Data Governance

Most of the cloud platforms provide encryption facilities, active directory, and network authentication protocols like Kerberos. In addition to this, cloud providers also offer data catalogs and indexing tools, but in many cases, organizations lack proper lineage.

Indeed, it a multifaceted process to perform cloud migration. But considering certain aspects like testing and validation, continuous integrations, interim state management, and cut-over plans of the platforms, can help to achieve a more efficient approach.

See Also: Top Cloud Storage Solution Companies