How efficient is your enterprise data management?
Maintaining an enterprise IT architecture is like owning an old car that is constantly in the shop: The extra costs add up, and the resources used up could be invested in a newer model for a better return on investment. Also, if you are an IT systems administrator limited to storage technology based on monolithic, proprietary hardware that is inefficient, costly and difficult to manage, you may struggle to not only catch up but also support data transformation initiatives.
If you're looking for a scalable enterprise data storage solution, it's critical to know if the storage you choose is designed to work with data and applications in its native form. We'll cover this in more detail below and outline some of the key considerations when evaluating your HPC workflows. All in all, this will help you select a solution that best meets the current and future needs of your business.
Evaluate your high-performance computing workflows
Most data comes from files created and accessed directly by native applications or mounted file systems. Working with this file data natively means using industry-standard protocols such as Network File System (NFS), Server Message Block (SMB), or Direct File System Pass (Block).
Data stored in its native format is considered unstructured data, meaning it lacks a predefined data model or schema and cannot be stored in a traditional relational database (more on this later). Because this type of unorganized data cannot simply be stored in a series of tables with columns and rows, organizations have complex and time-consuming data analysis processes required to gain valuable insights.
Analysts at Gartner estimate that unstructured data makes up a staggering 80% to 90% of all new enterprise data. That may sound surprising, but the reality is that enterprise data has been made up mostly of unstructured data for decades. In fact in 1998 Merrill Lynch claimed, "Unstructured data comprises the vast majority of data found in an organization, with some estimates as high as 80%." Lynch essentially made the assertion that as the volume of global data creation continues to grow year over year, the more important it becomes to have highly scalable enterprise data management solutions that can make effective and meaningful use of that data.
This "explosion of unstructured data" is being generated by video cameras, recording devices, satellites, sensors, genomic data, aerial imagery and other IoT-connected technologies - and represents a potential goldmine of insights.
Are you using your data in its native form?
Successful organizations are storing, managing and building high-performance computing (HPC) workflows and applications with file data in its native form using cloud object stores (such as Amazon S3 and Microsoft Azure) - and turning that data into value. These innovators use and manage data in all its forms to develop new business models, medical treatments, consumer products, business intelligence tools, and digital media.
Can you track and manage your unstructured data?
For many HPC organizations using legacy storage and cloud-native applications, the task of processing, managing and transforming unstructured data from a file to an object is a major challenge. Most technologies are not designed to solve this problem, which means enterprises must rebuild their architecture, redesign applications or use third-party data shift packages to generate value from their data - in many cases resulting in huge data silos with little visibility into that data . In addition, organizations are often limited to only certain protocols that may not be supported or appropriate for certain applications or end users. The difficult result for many leading companies around the world is that this valuable data is never used, is accessed inefficiently, and is often poorly understood.
In a 2019 NewVantage Partners' Big Data and AI executive survey, consisting of 64 C-level technology and business executives representing very large companies, 53% of survey respondents say "they are not yet treating data as a business asset." These alarming results come despite 92% of respondents indicating that the pace of their Big Data and artificial intelligence (AI) investments is accelerating.
Evaluate your organization's specific data storage needs
Enterprises needing to enable large datasets in HPC environments with unstructured data means that the ability to process and deliver data is part of their business. For this reason, when considering an optimal enterprise data storage solution, it is important to evaluate whether it meets your capacity, performance, data integrity and scale-out requirements needed to process data and potentially enable dense and high-performance workflows.
Evaluate enterprise data storage solutions that are ideal for your HPC workflows.
An optimal enterprise data storage solution should provide the infrastructure necessary to leverage HPC resources in their workflows. according to a Forbes survey, more than 95% of enterprises need to manage unstructured data, and by 150, more than 2025 trillions of gigabytes of data will need to be analyzed - meaning file storage will become more important than ever.
Efficient unstructured data management
Since unstructured data is primarily new data that is created on a daily basis, the more efficiently HPC organizations can consolidate, process and leverage this data, the more successful their results are likely to be. Not surprisingly, an ideal enterprise data storage solution should be designed to work natively with this type of data.
Object storage vs. file storage
In the modern cloud era, object storage is at the forefront of many organizations' minds, but most data is created and used as files. Object storage is an architecture that manages data as objects, as opposed to a storage architecture like a file system. File storage is a format or program for storing and managing data as a file hierarchy in which files are identifiable in a directory structure (generally displayed as a hierarchical tree structure).
File systems provide the basic abstraction of hierarchy that allows computers and humans to work with semantically interesting groupings of data. Sure, enterprise data storage users appreciate having a large storage container. However, object storage systems present a host of unforeseen next-generation problems; for example, object storage is not as powerful.
Get the guide: Download the Enterprise Data Storage Playbook
Evaluate your unstructured data management needs
Processing petabytes of data requires the right enterprise data storage solution based on the type of data being analyzed. For example, to process and analyze unstructured data that exists in the cloud and on-premises, organizations need a file data platform that can meet the needs of a hybrid storage infrastructure while providing real-time analytics and insights. When evaluating enterprise data storage types, it's more important than ever to select the solution that best meets your organization's needs today and in the future.
Align your HPC workflows with a modern enterprise storage solution
Maintaining an enterprise IT architecture is like owning an old car that is constantly in the shop: The extra costs add up, and the resources used up could be invested in a newer model for a better return on investment. Also, if you are an IT systems administrator limited to storage technology based on monolithic, proprietary hardware that is inefficient, costly and difficult to manage, you may struggle to not only catch up but also support data transformation initiatives.
If you're looking for a scalable enterprise data storage solution, it's critical to know if the storage you choose is designed to work with data and applications in its native form. We'll cover this in more detail below and outline some of the key considerations when evaluating your HPC workflows. All in all, this will help you select a solution that best meets the current and future needs of your business.
Evaluate your high-performance computing workflows
Most data comes from files created and accessed directly by native applications or mounted file systems. Working with this file data natively means using industry-standard protocols such as Network File System (NFS), Server Message Block (SMB), or Direct File System Pass (Block).
Data stored in its native format is considered unstructured data, meaning it lacks a predefined data model or schema and cannot be stored in a traditional relational database (more on this later). Because this type of unorganized data cannot simply be stored in a series of tables with columns and rows, organizations have complex and time-consuming data analysis processes required to gain valuable insights.
Analysts at Gartner estimate that unstructured data makes up a staggering 80% to 90% of all new enterprise data. That may sound surprising, but the reality is that enterprise data has been made up mostly of unstructured data for decades. In fact in 1998 Merrill Lynch claimed, "Unstructured data comprises the vast majority of data found in an organization, with some estimates as high as 80%." Lynch essentially made the assertion that as the volume of global data creation continues to grow year over year, the more important it becomes to have highly scalable enterprise data management solutions that can make effective and meaningful use of that data.
This "explosion of unstructured data" is being generated by video cameras, recording devices, satellites, sensors, genomic data, aerial imagery and other IoT-connected technologies - and represents a potential goldmine of insights.
Are you using your data in its native form?
Successful organizations are storing, managing and building high-performance computing (HPC) workflows and applications with file data in its native form using cloud object stores (such as Amazon S3 and Microsoft Azure) - and turning that data into value. These innovators use and manage data in all its forms to develop new business models, medical treatments, consumer products, business intelligence tools, and digital media.
Can you track and manage your unstructured data?
For many HPC organizations using legacy storage and cloud-native applications, the task of processing, managing and transforming unstructured data from a file to an object is a major challenge. Most technologies are not designed to solve this problem, which means enterprises must rebuild their architecture, redesign applications or use third-party data shift packages to generate value from their data - in many cases resulting in huge data silos with little visibility into that data . In addition, organizations are often limited to only certain protocols that may not be supported or appropriate for certain applications or end users. The difficult result for many leading companies around the world is that this valuable data is never used, is accessed inefficiently, and is often poorly understood.
In a 2019 NewVantage Partners' Big Data and AI executive survey, consisting of 64 C-level technology and business executives representing very large companies, 53% of survey respondents say "they are not yet treating data as a business asset." These alarming results come despite 92% of respondents indicating that the pace of their Big Data and artificial intelligence (AI) investments is accelerating.
Evaluate your organization's specific data storage needs
Enterprises needing to enable large datasets in HPC environments with unstructured data means that the ability to process and deliver data is part of their business. For this reason, when considering an optimal enterprise data storage solution, it is important to evaluate whether it meets your capacity, performance, data integrity and scale-out requirements needed to process data and potentially enable dense and high-performance workflows.
Evaluate enterprise data storage solutions that are ideal for your HPC workflows.
An optimal enterprise data storage solution should provide the infrastructure necessary to leverage HPC resources in their workflows. according to a Forbes survey, more than 95% of enterprises need to manage unstructured data, and by 150, more than 2025 trillions of gigabytes of data will need to be analyzed - meaning file storage will become more important than ever.
Efficient unstructured data management
Since unstructured data is primarily new data that is created on a daily basis, the more efficiently HPC organizations can consolidate, process and leverage this data, the more successful their results are likely to be. Not surprisingly, an ideal enterprise data storage solution should be designed to work natively with this type of data.
Object storage vs. file storage
In the modern cloud era, object storage is at the forefront of many organizations' minds, but most data is created and used as files. Object storage is an architecture that manages data as objects, as opposed to a storage architecture like a file system. File storage is a format or program for storing and managing data as a file hierarchy in which files are identifiable in a directory structure (generally displayed as a hierarchical tree structure).
File systems provide the basic abstraction of hierarchy that allows computers and humans to work with semantically interesting groupings of data. Sure, enterprise data storage users appreciate having a large storage container. However, object storage systems present a host of unforeseen next-generation problems; for example, object storage is not as powerful.
Get the guide: Download the Enterprise Data Storage Playbook
Evaluate your unstructured data management needs
Processing petabytes of data requires the right enterprise data storage solution based on the type of data being analyzed. For example, to process and analyze unstructured data that exists in the cloud and on-premises, organizations need a file data platform that can meet the needs of a hybrid storage infrastructure while providing real-time analytics and insights. When evaluating enterprise data storage types, it's more important than ever to select the solution that best meets your organization's needs today and in the future.
Align your HPC workflows with a modern enterprise storage solution
Legacy File Storage Systems
Legacy file storage systems rely on a block device as the abstraction layer for the hardware responsible for storing and retrieving desired blocks of data; however, the block size in a file system can be a multiple of the physical block size. This leads to a lack of scalability and space inefficiency due to internal fragmentation, as file lengths are often not integer multiples of the block size; thus, the last block of a file may be left partially empty. This leads to fragmentation where storage space is used inefficiently, reducing capacity and performance.
Legacy object storage systems
Legacy object storage systems
Some organizations are trying to adopt this Legacy object storage systems as a solution to the challenges of scaling and geo-distributing unstructured data. However, using object storage in use cases for which it was never intended is technically ill-suited. To achieve this, object storage intentionally compromises features that many users need and expect: transactional consistency, file modification, fine-grained access control, and use of standard protocols such as NFS and SMB, to name a few. Object storage also leaves the problem of organizing data intact; instead, users are encouraged to index the data themselves in some sort of external database. This may be sufficient for the storage needs of standalone applications, but it makes collaboration between applications and between people and those applications difficult.
There is a surprising amount of valuable business logic encoded in the directory structure of enterprise file systems. Therefore, the need for large-scale file storage remains compelling.
Modern HPC workflows
There is a surprising amount of valuable business logic encoded in the directory structure of enterprise file systems. Therefore, the need for large-scale file storage remains compelling.
Modern HPC workflows
Modern HPC workflows are almost always applications that were developed independently but interoperate by exchanging file-based data, an interop scenario that is simply not possible with object storage. In addition, object stores do not provide the governance benefits of a file system.
Modern file storage systems
Modern file storage systems
Modern file storage systems, as well as Qumulo Core, attempted to solve this problem through a technique called . Scalable Block Storage (SBS). The Qumulo file system is based on SBS, a virtualized block layer that leverages the principles of massively scalable distributed databases and is optimized for the unique requirements of file-based data.
From a block storage perspective, the SBS is the block layer of the Qumulo file system and its underlying mechanism for storing data, giving the file system massive scalability, optimized performance and data protection. Time-consuming tasks such as protecting, rebuilding, and deciding which disks contain which data take place in the SBS layer beneath the file system. This allows unstructured data files to be extracted into a hierarchical file system type layout that combines the best of file system architecture and block storage architecture.
The virtualized protected block functionality of SBS is a major advantage for the Qumulo file system. Because the Qumulo file system uses block-based protection, small files are just as efficient as large files. The result is a file system with unmatched scaling capabilities. In contrast, legacy storage devices simply weren't designed to handle the massive size of today's data footprint, which uses inefficient mirroring for small files and system metadata.
Is scale-out network attached storage (NAS) the future of enterprise data storage management (EDM)?
Scale-out Network Attached Storage NASTraditional scale-up and scale-out file systems are not capable of meeting the emerging demands of managing on-premise and cloud storage at scale. The engineers who developed them 20 years ago never expected the number of files and directories and varying file sizes that characterize modern workloads. Nor could they have anticipated cloud computing.
The rise of unstructured data
From a block storage perspective, the SBS is the block layer of the Qumulo file system and its underlying mechanism for storing data, giving the file system massive scalability, optimized performance and data protection. Time-consuming tasks such as protecting, rebuilding, and deciding which disks contain which data take place in the SBS layer beneath the file system. This allows unstructured data files to be extracted into a hierarchical file system type layout that combines the best of file system architecture and block storage architecture.
The virtualized protected block functionality of SBS is a major advantage for the Qumulo file system. Because the Qumulo file system uses block-based protection, small files are just as efficient as large files. The result is a file system with unmatched scaling capabilities. In contrast, legacy storage devices simply weren't designed to handle the massive size of today's data footprint, which uses inefficient mirroring for small files and system metadata.
Is scale-out network attached storage (NAS) the future of enterprise data storage management (EDM)?
Scale-out Network Attached Storage NASTraditional scale-up and scale-out file systems are not capable of meeting the emerging demands of managing on-premise and cloud storage at scale. The engineers who developed them 20 years ago never expected the number of files and directories and varying file sizes that characterize modern workloads. Nor could they have anticipated cloud computing.
The rise of unstructured data
Enterprises are increasingly relying on unstructured data storage management (EDM) for regulation, analytics and decision making. Unstructured data is the backbone of analytics, machine learning and business intelligence.
Enterprise data management (EDM) requires scalability.
Enterprise data management (EDM) requires scalability.
Enterprises that need to enable large data sets in HPC environments with unstructured data means that the ability to process and provision data is part of their business. For this reason, enterprise IT systems and storage administrators are looking for a solution designed to work natively with this type of data. The ideal storage solution for this meets their capacity, performance, data integrity and scale-out requirements needed to process data and serve potentially dense and high-performance workflows.
Scalable enterprise data storage solutions with scale-out NAS.
Scalable enterprise data storage solutions with scale-out NAS.
Qumulo was founded in 2012 as the file storage crisis reached its tipping point. A group of storage pioneers, the inventors of scale-out NAS, came together to create a different kind of storage company that vigorously addressed these new demands. The result of their work and the team they assembled is Qumulo, which has developed the world's first enterprise-proven hybrid cloud file storage system that spans the data center, private clouds and public clouds. It scales to billions of files, costs less and has a lower total cost of ownership (TCO) than traditional storage solutions. Real-time analytics allow administrators to easily access and manage data regardless of size or location. Qumulo's continuous replication allows data to be moved where it's needed, when it's needed. B. between clusters running on-premises and in the cloud, or between clusters running on different cloud instances.
Choosing the right enterprise data storage solution
Choosing the right enterprise data storage solution
With this brief overview of how to evaluate enterprise data storage solutions and compare those solutions, you should now have a better understanding of how to select an ideal data storage solution based on the types of data your organization stores. For more insights, check out Part 2 of this series, where we provide a more thorough comparison of the different data storage types: Block Storage vs. Object Storage vs. File Storage.
This article is just the first in a 4-part series on Why Enterprises Should Consider File Data When Evaluating Enterprise Data Storage Solutions-and has only scratched the surface on these important considerations. To learn more, Download our new Enterprise Playbook for our most comprehensive guide to choosing the right data storage solution to handle the explosion of unstructured data.
This article is just the first in a 4-part series on Why Enterprises Should Consider File Data When Evaluating Enterprise Data Storage Solutions-and has only scratched the surface on these important considerations. To learn more, Download our new Enterprise Playbook for our most comprehensive guide to choosing the right data storage solution to handle the explosion of unstructured data.