Big data storage is a storage infrastructure that is designed specifically to store, manage, and retrieve massive amounts of data, or big data. Big data storage enables the storage and sorting of big data in such a way that it can easily be accessed, used, and processed by applications and services working on big data. Big data storage is also able to flexibly scale as required.
Big data storage primarily supports storage and input/output operations on storage with a very large number of data files and objects. A typical big data storage architecture is made up of a redundant and scalable supply of direct attached storage (DAS) pools, scale-out or clustered network attached storage (NAS) or an infrastructure based on object storage format. The storage infrastructure is connected to computing server nodes that enable quick processing and retrieval of big quantities of data
The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud. In this paper, we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers. Instead of protecting the big data itself, the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function. Analysis, comparison, and simulation prove that the proposed scheme is efficient and secure for the big data of Cloud tenants.
Cloud offers exceptional flexibility, allowing organizations to add big data analytics to their capabilities. Investments in big data and analytics can be very essential to drive efficient and cost-effective infrastructure.
Cloud computing models can accelerate the potential for scalable big data solutions. Cloud offers flexibility to access data, deliver insights, and drive value. However, cloud-enabled big data analytics is not a one-size-fits-all solution.
As enterprises look to accelerate their digital transformation, they must analyze and leverage the vast amount of data that has become available for effective decision making. By leveraging cloud-based analytics with scalable, persistent cloud storage, companies can unshackle their data and develop new business insights.
Using NYGCI’s Object Storage, organizations can build a centralized data repository, leveraging cost-effective and scalable storage that makes it possible to collect and store nearly unlimited amounts of data of any type, from any source.
With our STaaS reduce big data storage costs by 60%+ while increasing storage scalability.
- 60+% TCO savings vs indexer-based storage
- On-prem data lake with exabyte scalability
- Modular design for non-disruptive storage growth
- Integrates seamlessly with AWS, GCP, and Azure
- Deploy as software or appliance(s)