Cloud Space Optimization
Rapid change is the new normal as organizations embrace the cloud.
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:
- Segregated repeatable architecture/design patterns and types of processing that might need a different handling of storage type (See Figure 1).
- Understand the key criteria for processing, data access and performance of the application from the client like availability, durability, and scalability.
- Rationalize the criteria and come up with a recommendation of storage type by the patterns.
- 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.
- Come up with a recommended matrix of storage type by cloud providers, mapped to the given criteria.
Data storage is no longer a warehousing issue; implicit in the new world of data everywhere is the implied ability to find, access, and use that data in an efficient manner. Much of that data exists on a cloud, so you need to know how to make sure data storage is optimized and doesn’t become a weak link in your cloud platform. Storage techniques and software tools can help you achieve data and database optimization, and help to manage virtualized data storage through the software layer. This article explores what storage optimization tools can do for you.
Given the vast amounts of data being created daily throughout the world, it is not surprising that businesses are seeking more efficient and cost-effective means of storing data. Once the era of cloud computing emerged, the world needed lots more storage at a low price point; then the cost of storage media declined. But two newer trends, big data analysis and the Internet of Things, threaten to override the savings from lower storage media costs simply because the massive datahandling requirements can be overwhelming.
Features of cloud-optimized storage
You can categorize three major methods for optimizing storage for a cloud system: Optimizing the data, optimizing the database, and implementing software-defined storage.
