Top Ten Data Storage Tools
There are a large number of big data storage products on the market. Which products are the best? Obviously, there is no simple answer. Choosing a big data storage tool involves many changing factors, including the existing environment, current storage platform, data growth expectations, file size and type, database and application program combinations, etc.
Hitachi
Hitachi provides several big data storage products. Big data analysis tools, Hitachi Super Scale-Out Platform (HSP), HSP technology architecture, and Hitachi Video Management Platform (VMP) developed in cooperation with Pentaho Software. The latter example is specifically for big video, an ascendant subset of big data, for video surveillance and other video-intensive storage applications.DDN
Similarly, DataDirect Networks (DDN) also has a number of solutions for big data storage.For example, its high-performance SFA7700X file storage can be automatically layered to the WOS object storage archiving system, supporting rapid collection, simultaneous analysis, and cost-effective retention of big data.
Spectra BlackPearl
Spectra Logic's BlackPearl deep storage gateway provides object storage interfaces for SAS-based disks, SMR reduced-speed disks, or tapes. All these technologies can be placed behind BlackPearl in the storage environment.Kaminario K2
Kamiario provides another big data storage platform. Although it does not provide classic big data devices, its all-flash arrays are finding a place in many big data applications.Caringo
Caringo was founded in 2005 to discover the value of data and solve large-scale data protection, management, organization, and search problems. With the flagship product Swarm, users can achieve long-term storage, delivery and analysis without having to migrate data to different solutions, thus reducing the total cost of ownership. It has been used by more than 400 organizations worldwide, such as the US Department of Defense, the Brazilian Federal Court System, the City of Austin, Telefónica, British Telecom, Ask.com, and Johns Hopkins University.Infogix
The Infogix enterprise data analysis platform is based on five core functions: data quality, transaction monitoring, balance and coordination, identity matching, behavior analysis, and predictive models. These features are said to help companies improve operational efficiency, generate new revenue, ensure compliance, and gain a competitive advantage. The platform can detect data errors in real-time and automatically perform a comprehensive analysis to optimize the performance of big data projects.Avere Hybrid Cloud
Avere provides another big data storage solution. Its Avere hybrid cloud is deployed in various use cases in hybrid cloud infrastructure. Physical FXT clusters are used for NAS optimization for this use case, making full use of the all-flash high-performance layer in front of existing disk-based NAS systems. FXT clusters use cache to automatically speed up active data, use clusters to expand performance (add more processors and memory) and capacity (add more solid-state drives), and hide the latency of core storage sometimes deployed on the WAN. Users find it to be a good way to speed up rendering, genome analysis, financial simulation, software tools, and binary code libraries.In the use case of private object-oriented file storage, users want to migrate from NAS to private object storage. They tend to like the efficiency, simplicity, and flexibility of private objects, but they don't like their performance or object-based API interfaces. In this use case, the FXT cluster improves the performance of private object storage in the same way as NAS optimization.
Finally, the use case of a cloud storage network is similar to the use case of private object-oriented file storage. An added benefit is that enterprises can start to build fewer data centers and migrate data to the cloud. Latency is one of the challenges to be overcome for this use case, which is exactly what the physical FXT cluster has to solve. During access, the data is cached locally on the FXT cluster, so that all subsequent accesses have the advantage of low latency. The FXT cluster may have a total cache capacity of up to 480TB, so a large amount of data can be stored locally to avoid cloud delays.
DriveScale
Big data is usually stored on local disks, which means that in order to achieve efficiency and scalability when the scale of big data clusters continues to expand, it is necessary to maintain the logical relationship between computing and storage. So a question arises: How to separate the disk from the server and continue to provide the same logical relationship between the processor/memory combination and the drive? How to achieve the cost, scale, and manageability of the shared storage pool Efficiency while still providing the benefits of locality? It is said that DriveScale can do this by using Hadoop data storage.However, storage professionals who want to install and manage resources for big data applications are mainly constrained by the Hadoop architecture, which itself is optimized for local drives on the server. As the amount of data continues to increase, the only way is to purchase more and more servers, not only to meet computing needs but also to provide greater storage capacity. DriveScale allows users to purchase storage capacity independently of computing capacity so that the capacity is just right at each level.
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