Bringing all your data together shares similarities with conducting an orchestra. Instead of combining the violin, oboe, and cello, this brand of orchestration combines distributed data types from different places, platforms, and locations working as a cohesive entity presented to applications or users anywhere. That’s because historically, accessing high-performance data outside of your computer network was inefficient. Because the storage infrastructure existed in a silo, systems like HPC Parallel (which lets users store and access shared data across multiple networked storage nodes), Enterprise NAS (which allows large-scale storage and access to other networks), and Global Namespace (virtually simplifies network file systems) were limited when it came to sharing. Because each operated independently, the data within each system was siloed making it a problem collaborating with data sets over multiple locations.
Collaboration was possible, but too often you lost the ability to have high performance. This Boolean logic decreased potential because having an IT architecture that supported both high performance and collaboration with data sets from different storage silos typically became an either/or decision: You were forced to choose one but never both.
What is data orchestration?
Data orchestration is the automated process of taking siloed data from multiple data storage systems and locations, combining and organizing it into a single namespace. Then a high-performance file system can place data in the edge service, data center, or cloud service most optimal for the workload.The recent rise of data analytic applications and artificial intelligence (AI) capabilities has accelerated the use of data across different locations and even different organizations. In the next data cycle, organizations will need both high-performance and agility with their data to compete and thrive in a competitive environment.
That means data no longer has a 1:1 relationship with the applications and compute environment that generated it. It needs to be used, analyzed, and repurposed with different AI models and alternate workloads, and across a remote, collaborative environment.
Hammerspace’s technology makes data available to different foundational models, remote applications, decentralized compute clusters, and remote workers to automate and streamline data-driven development programs, data insights, and business decision making. This capability enables a unified, fast, and efficient global data environment for the entire workflow — from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds.
Control of enterprise data services for governance, security, data protection, and compliance can now be implemented globally at a file-granular level across all storage types and locations. Applications and AI models can access data stored in remote locations while using automated orchestration tools to provide high-performance local access when needed for processing. Organizations can grow their talent pools with access to team members no matter where they reside.
Decentralizing the data center
Data collection has become more prominent, and the traditional system of centralized data management has limitations. Issues of centralized data storage can limit the amount of data available to applications. Then, there are the high infrastructure costs when multiple applications are needed to manage and move data, multiple copies of data are retained in different storage systems, and more headcount is needed to manage the complex, disconnected infrastructure environment. Such setbacks suggest that the data center is no longer the center of data and storage system constraints should no longer define data architectures.Hammerspace specializes in decentralized environments, where data may need to span two or more sites and possibly one or more cloud providers and regions, and/or where a remote workforce needs to collaborate in real time. It enables a global data environment by providing a unified, parallel global file system.
Enabling a global data environment
Hammerspace completely revolutionizes previously held notions of how unstructured data architectures should be designed, delivering the performance needed across distributed environments to- Free workloads from data silos.
- Eliminate copy proliferation.
- Provide direct data access through local metadata to applications and users, no matter where the data is stored.
The days of enterprises struggling with a siloed, distributed, and inefficient data environment are over. It’s time to start expecting more from data architectures with automated data orchestration. Find out how by downloading Unstructured Data Orchestration For Dummies, Hammerspace Special Edition, here.
