Five keys to cloud storage optimization

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As enterprises migrate to the cloud, data management has emerged as a crucial consideration. Many options are available, so it is important for enterprises to know exactly how they want to structure and store their data. Above all, you need to establish the criteria you are looking for, in order to effectively evaluate what kind of storage needs to be used. Have your data strategy clearly defined as to what workloads need to be migrated to the cloud before choosing a storage type.

Many enterprises are turning to cloud storage solutions because it is viewed as inexpensive, but you need to make sure that application performance is not compromised. Cloud storage can be used for a wide variety of workloads such as archiving and disaster recovery, and there are different proposed tiers of storage depending on the workload.

Storage type plays a key role in the decision process and is determined by different storage patterns, as follows:

By system:

  • Data in motion: Data in queue needs to be persisted for ‘time to live’ and replicated for resilience.
  • DataLake: Large volumes of data with staging, transformation, harmonization, and scale-out compute for specific workloads
  • Analytical data store: More for analytical data access, it combines with dedicated workloads to meet SLAs.
  • Operational data store (ODS): Data storage which is more transactional in nature, with frequent updates and deletions
  • NoSQL: Optimally store different data types such as XML and content store

By function:

  • Scale-out storage: Need for scaling out for certain workloads with large data volume, for a limited timeframe, once output is persisted, scale down to a steady state
  • Perspective/business functions: Data can be optimally partitioned and stored in different physical manifestations, designing an optimal access path
  • Archive: Data that is beyond a retention period of the data topic within the system
  • In memory compute: For near real-time access to data
  • Cache: For SLA-driven access to components/data

Cloud storage optimization: Five key considerations

A standard methodology needs to be created to associate storage with a particular processing type. Here are five key considerations to determine your optimal cloud storage:

  1. Segregated repeatable architecture/design patterns and types of processing that might need a different handling of storage type (See Figure 1).
  2. Understand the key criteria for processing, data access and performance of the application from the client like availability, durability, and scalability.
  3. Rationalize the criteria and come up with a recommendation of storage type by the patterns.
  4. Design for federated data access as you will most likely end up accessing data across platforms that are on-premise and in the cloud for certain capabilities.
  5. Come up with a recommended matrix of storage type by cloud providers, mapped to the given criteria (see Figures 2 and 3).

When moving storage to the cloud, enterprises need to strike the perfect balance between reducing OPEX and meeting business needs. At DXC, we have extensive experience with helping enterprises choose and implement the right cloud storage solution. To learn more, visit dxc.technology/cloud.

Figure 1 – Use case illustration from specific customer requirement

Figure 2 – Azure Storage Recommendation for given customer requirements

Figure 3 – AWS Storage Recommendation for given customer requirements

(Note: For Figures 1-3, availability, throughput and scalability are rated on a 1-5 scale.)


Srijani Dey is Chief Architect of Big Data for DXC’s Analytics practice. She is a turnaround specialist with extensive experience in leading teams from vision through design to execution of complex, highly scalable data management and analytics solutions that leverage the latest technology innovations.

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