Technology

The DataSphere architecture leverages numerous technologies to simplify operations for customers while helping enterprises seamlessly align the right data to the right storage at the right time. From its out-of-band operation to its optimization of industry-standard protocols, DataSphere is designed to finally overcome the limitations of traditional storage architectures to help petabyte-scale enterprises respond to changing business demands.

Global Namespace
Objective Data Management
Cross-Array Storage Tiering
Out-of-band Operation
Standard Protocols

Connect Storage Resources in a Global Namespace

DataSphere pools multiple physical storage resources and presents a virtualized single logical namespace to clients. The global namespace greatly simplifies management, while using open standards-based protocols to easily connect clients.

Add Intelligence to Data Management with Objectives

To dynamically and automatically respond to evolving business demands, DataSphere uses objectives to set an application’s data performance, cost and reliability goals throughout its operational life. Managing by objectives ensures the right data is on the right storage at the right time.

Maximize the Unique Features of Each Storage Resource

Despite the numerous options available today, storage fits within well-defined operational characteristics or attributes. For example, server-side flash storage is very fast (providing low latency, high IOPS, high bandwidth), is considered less reliable (in the event of a hardware failure), and carries a premium when compared to industry standard networked storage. NAS filers and SAN arrays have lower performance, but higher levels of data reliability through sophisticated RAID operations, error correction schemes, or disaster recovery redundancy. In recent years, cloud-based object storage has provided lower cost and greater capacity, but by comparison, deliver the lowest performance, which makes it more suitable for colder data and archiving applications. Each of these operating attributes can be used to define data management objectives.

Automatic Data Movement Across Storage to Align the Right Resource for the Job

With DataSphere, admins can create objectives with specific storage capabilities required to meet business needs. Target objectives can then be selected from a catalog of offerings with matching storage capabilities and applied to single files, directories, or shares, giving unprecedented application price/performance control. For example, a “Platinum” objective level can define a storage requirement with the highest IOPS, lowest latency and highest bandwidth for temp space, logs, indexes or swap space. In contrast, a “Bronze” objective would place less active data on lower cost and lower performant stores. DataSphere continually analyzes if objectives are being met, and will automatically move data to maintain compliance.

Storage Choice and Data Tiering

Thanks to a wide range of capabilities across performance, protection and price, today’s IT professionals have more choice than ever before when selecting a storage type or vendor to meet an application or business need. Given the storage diversity found in most petabyte-scale enterprises today, the challenge for IT is quickly becoming how to ensure the right resource is serving the right data at the right time.

Flash in a server is an ultra-fast storage memory that can be attached via PCI-Express to serve as a very low latency, high IOPS, direct-attached storage tier, but it comes at a premium cost. Network-attached flash in an array also brings more performance to primary storage at a high cost. Classic shared or networked NAS and SAN storage are known for high reliability and capacity, and cloud storage fulfills expandability at low costs, but with lower access or near-line performance for cold data and archiving functions. Each of these storage types provide a unique price-performance capability with different levels of data reliability, and the choices get even broader when considering emerging technologies.

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In either case, no matter the type or vendor, DataSphere pools these heterogeneous storage resources together and present a virtualized view of data under a single namespace to clients. This global namespace greatly simplifies data management, while using open standards-based protocols to transparently present to the clients running the applications.

Combine Different Storage to Tier Data or Scale Out

Within the global namespace, DataSphere gives IT the power to configure Data Flow architectures to automatically deliver a variety of capabilities. Once storage can be classified by its price/performance and reliability attributes and its data virtualized, IT can now consider several different configurations to move and place data without impacting or changing applications.

Traditional data migration is the simple act of moving from old to new storage. However, with DataSphere objectives and client performance telemetry analysis, data can intelligently migrate to the appropriate storage; cold data to cloud, warm data to NAS arrays, and hot data to a all-flash storage. Storage can now be tiered support data throughout its lifecycle from creation to long term archival.

When several NAS systems are clustered together, IT can scale-out performance and accelerate metadata accesses from data. For data intensive applications, files can be load balanced across separate NAS devices to allow parallel access for the highest level of I/O performance. When this architecture integrates the cloud, IT has the ability to archive cold data across multiple cloud providers, while even allowing promotion of data back from the cloud to higher performing storage, automatically.

The Benefits of Out-of-Band Operation and Data Access

In modern enterprise architectures, out-of-band management is the separation of administration from application data. DataSphere leverages this architectural approach because of its many significant advantages over in-band or gateway based solutions. These include:

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  • Native Data Access: Applications do not see increased latency because they directly access storage devices containing the data, rather than passing through a gateway or agent.
  • Scalable: File-granular load balancing with parallel access across multiple stores increases application performance.
  • Fast Metadata Performance: With dedicated metadata servers, metadata operations are never stuck behind data payloads and are always executed with low-latency performance. Focusing only on metadata without the burden of data requests allows DataSphere to support billions of data objects within a single namespace.
  • Virtualizing the View of Data: DataSphere creates a global namespace with a unified view of application data on top of heterogeneous storage.
  • Data Orchestration: By virtualizing the view of data, DataSphere gains the ability to move data between different storage tiers without application disruption.
  • Storage Agnostic: Operating out-of-band enables DataSphere to support any storage type, from any vendor, for unprecedented choice and flexibility in meeting business needs.
  • Highly Reliable: With DataSphere, data integrity continues to be fulfilled by the designated storage devices. If you have invested in a reliable, redundant storage system, you continue to get all its benefits. DataSphere knows the capabilities of the storage and will place data on systems that can meet data policies.

By separating the control plane from the data plane, DataSphere can achieve enterprise-class, mission critical reliability and scalability while ensuring performance even while applications are running and data is in motion.

Avoid Agents with Industry-Standard Protocols

Standards-based protocols simplify access, adoption, and customers use, and also provide a vendor-independent, future-proof method to access client data. DataSphere supports several standards-based protocols to capture telemetry on how an application uses its data or to virtualize a client's view of its data. This avoids the common pitfall found in solutions that require the installation of an agent or a custom driver, which can make managing updates over time extremely difficult for IT to manage across thousands or tens of thousands of clients.

DataSphere supports two major protocol families for accessing managed data: NFS (Network File System) and SMB (Server Message Block). Any data under management, no matter what type of storage it is stored on, can be accessed using either protocol.

DataSphere does not require the users to store their data using the same protocol as the protocol used for access. For example, SMB access to a file is fully supported when the file is stored using NFS v3 or even Amazon S3 object protocols. The DataSphere Extended Services (DSX) nodes in a DataSphere environment act as the protocol access points for NFS v3 and SMB clients, allowing the environment to scale with as many access points as required.

For native support, NFS v4.2 clients can connect directly to multiple storage devices and scale to higher I/O performance with parallel file access. Native access with NFS v4.2 also enables additional enhancements, such as offloaded file cloning and space efficiency improvements. DataSphere supports backend storage volumes using either NFS v3, block or Amazon S3 compatible protocols.

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