Tag Archives: Storage

7 Enterprise Storage Trends for 2018


Enterprises today are generating and storing more data than ever, and the trend shows no sign of slowing down. The rise of big data, the internet of things, and analytics are all contributing to the exponential data growth. The surge is driving organizations to expand their infrastructure, particularly data storage.

In fact, the rapid growth of data and data storage technology is the biggest factor driving change in IT infrastructure, according to the Interop ITX and InformationWeek 2018 State of Infrastructure study. Fifty-five percent of survey respondents choose it as one of the top three factors, far exceeding the need to integrate with cloud services.

Organizations have been dealing with rapid data growth for a while, but are reaching a tipping point, Scott Sinclair, senior analyst at ESG, said in an interview.

“If you go from 20 terabytes to 100 terabytes, that’s phenomenal growth but from a management standpoint, it’s still within the same operating process,” he said. “But if you go from a petabyte to 10 or 20 petabytes, now you start taking about a fundamentally different scale for infrastructure.”

Moreover, companies today see the power of data and understand that they need to harness it in order to become competitive, Sinclair said.

“Data has always been valuable, but often it was used for a specific application or workload. Retaining data for longer periods was more about disaster recovery, having an archive, or for regulatory compliance,” he said. “As we move more into the digital economy, companies want to leverage data, whether it’s to provide more products and services, become more efficient, or better engage with their customers.”

To support their digital strategy, companies are planning to invest in more storage hardware in their data centers, store more data in the cloud, and investigate emerging technologies such as software-defined storage, according to the 2018 State of Infrastructure study. Altogether, they’re planning to spend more on storage hardware than other infrastructure.

Read on for more details from the research and to find out about enterprise storage plans for 2018. Click on the row of buttons below or on the arrows on either side of the images. For the full survey results, download the complete report.

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Choosing a Cloud Provider: 8 Storage Considerations


Amazon Web Services, Google, and Azure dominate the cloud service provider space, but for some applications it may make sense to choose a smaller provider specializing in your app class and able to deliver a finer-tuned solution. No matter which cloud provider you choose, it pays to look closely at the wide variety of cloud storage services they offer to make sure they will meet your company’s requirements.

There are two major classes of storage with the big cloud providers, which offer local instance storage with selected instances, as well as a selection of network storage options for permanent storage and sharing between instances.

As with any storage, performance is a factor in your decision-making process. There are many shared network storage alternatives, including storage tiers from really hot to freezing cold and within the top tiers, differences depending on choice of replica count, and variations in prices for copying data to other spaces.

The very hot tier is moving to SSD and even here there are differences between NVMe and SATA SSDs, which cloud tenants typically see as IOPS levels. For large instances and GPU-based instances, the faster choice is probably better, though this depends on your use case.

At the other extreme, the cold and “freezing” storage, the choices are disk or tape, which impacts data retrieval times. With tape, that can take as much as two hours, compared with just seconds for disk.

Data security and vendor reliability are two other key considerations when choosing a cloud provider that will store your enterprise data.  Continue on to get tips for your selection process.

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Big Data Storage: 7 Key Factors


Defining big data is actually more of a challenge than you might think. The glib definition talks of masses of unstructured data, but the reality is that it’s a merging of many data sources, both structured and structured, to create a pool of stored data that can be analyzed for useful information.

We might ask, “How big is big data?” The answer from storage marketers is usually “Big, really big!” or “Petabytes!”, but again, there are many dimensions to sizing what will be stored. Much big data becomes junk within minutes of being analyzed, while some needs to stay around. This makes data lifecycle management crucial. Add to that globalization, which brings foreign customers to even small US retailers. The requirements for personal data lifecycle management under the European Union General Data Protection Regulation go into effect in May 2018 and penalties for non-compliance are draconian, even for foreign companies, at up to 4% of global annual revenues per affected person.

For an IT industry just getting used to the term terabyte, storing petabytes of new data seems expensive and daunting. This would most definitely be the case with RAID storage array; in the past, an EMC salesman could retire on the commissions from selling the first petabyte of storage. But today’s drives and storage appliances have changed all the rules about the cost of capacity, especially where open source software can be brought into play.

In fact, there was quite a bit of buzz at the Flash Memory Summit in August about appliances holding one petabyte in a single 1U rack. With 3D NAND and new form factors like Intel’s “Ruler” drives, we’ll reach the 1 PB goal within a few months. It’s a space, power, and cost game changer for big data storage capacity.

Concentrated capacity requires concentrated networking bandwidth. The first step is to connect those petabyte boxes with NVMe over Ethernet, running today at 100 Gbps, but vendors are already in the early stages of 200Gbps deployment. This is a major leap forward in network capability, but even that isn’t enough to keep up with drives designed with massive internal parallelism.

Compression of data helps in many big data storage use cases, from removing repetitive images of the same lobby to repeated chunks of Word files. New methods of compression using GPUs can handle tremendous data rates, giving those petabyte 1U boxes a way of quickly talking to the world.

The exciting part of big data storage is really a software story. Unstructured data is usually stored in a key/data format, on top of traditional block IO, which is an inefficient method that tries to mask several mismatches. Newer designs range from extended metadata tagging of objects to storing data in an open-ended key/data format on a drive or storage appliance. These are embryonic approaches, but the value proposition seems clear.

Finally, the public cloud offers a home for big data that is elastic and scalable to huge sizes. This has the obvious value of being always right-sized to enterprise needs and AWS, Azure and Google have all added a strong list of big data services to match. With huge instances and GPU support, cloud virtual machines can emulate an in-house server farm effectively, and make a compelling case for a hybrid or public cloud-based solution.

Suffice to say, enterprises have a lot to consider when they map out a plan for big data storage. Let’s look at some of these factors in more detail.

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Object Storage: 8 Things to Know


Object storage is one of the hottest technology trends, but it isn’t a particularly new idea: The concept surfaced in the mid-90s and by 2005 a number of alternatives had entered the market. Resistance from the entrenched file (NAS) and block (SAN) vendors, coupled with a new interface method, slowed adoption of object storage. Today, with the brilliant success of Amazon Web Services’ S3 storage system, object storage is here to stay and is making huge gains against older storage methods.

Object storage is well suited to the new data environment. Unstructured data, which includes large media files and so-called big data objects, is growing at a much faster rate than structured data and, overall, data itself is growing at a phenomenal rate.

Experience has taught us that traditional block systems become complex to manage at a relatively low scale, while the concept of creating a single pool of data breaks down as the number of appliances increases, especially if the pool crosses the boundaries of different equipment types. Filers have hierarchies of file folders which become cumbersome at scale, while today’s thousands of virtual instances make file-sharing systems clumsy.

An inherent design feature of object stores is distribution of objects across all of the storage devices, or at least into subsets if there is a large number of devices in the cluster. This removes a design weakness of the block/file approach, where failure in an appliance or in more than a single drive could cause either a loss of data availability or even loss of data itself.

Object stores typically use an algorithm such as CRUSH to spread chunks of a data object out in a known and predictable way. Coupling this with replication, and more recently with erasure coding, means that several nodes or drives can fail without materially impacting data integrity or access performance. The object approach also effectively parallelizes access to larger objects, since a number of nodes will all be transferring pieces of the object at the same time.

There are now a good number of software-only vendors today, all of which are installable on a wide variety of COTS hardware platforms. This includes the popular Ceph open source solution, backed by Red Hat. The combination of any of these software stacks and low-cost COTS gear makes object stores attractive on a price-per-terabyte basis, compared to traditional proprietary NAS or SAN gear.

Object storage is evolving to absorb the other storage models by offering a “universal storage” model where object, file and block access portals all talk to the same pool of raw object storage.  Likely, universal storage will deploy as object storage, with the other two access modes being used to create a file or block secondary storage to say all-flash arrays or filers. In the long term, universal storage looks to be the converging solution for the whole industry.

This trend is enhanced by the growth of software-defined storage (SDS). Object stores all run natively in a COTS standard server engine, which means the transition from software built onto an appliance to software virtualized into the instance pool is in most cases trivial. This is most definitely not the case for older proprietary NAS or SAN code. For object stores, SDS makes it possible to scale services such as compression and deduplication easily. It also opens up rich services such as data indexing.

Continue on to get up to speed on object storage and learn how it’s shaking up enterprise storage.

(Image: Kitch Bain/Shutterstock)



Source link

Object Storage: 8 Things to Know


Object storage is one of the hottest technology trends, but it isn’t a particularly new idea: The concept surfaced in the mid-90s and by 2005 a number of alternatives had entered the market. Resistance from the entrenched file (NAS) and block (SAN) vendors, coupled with a new interface method, slowed adoption of object storage. Today, with the brilliant success of Amazon Web Services’ S3 storage system, object storage is here to stay and is making huge gains against older storage methods.

Object storage is well suited to the new data environment. Unstructured data, which includes large media files and so-called big data objects, is growing at a much faster rate than structured data and, overall, data itself is growing at a phenomenal rate.

Experience has taught us that traditional block systems become complex to manage at a relatively low scale, while the concept of creating a single pool of data breaks down as the number of appliances increases, especially if the pool crosses the boundaries of different equipment types. Filers have hierarchies of file folders which become cumbersome at scale, while today’s thousands of virtual instances make file-sharing systems clumsy.

An inherent design feature of object stores is distribution of objects across all of the storage devices, or at least into subsets if there is a large number of devices in the cluster. This removes a design weakness of the block/file approach, where failure in an appliance or in more than a single drive could cause either a loss of data availability or even loss of data itself.

Object stores typically use an algorithm such as CRUSH to spread chunks of a data object out in a known and predictable way. Coupling this with replication, and more recently with erasure coding, means that several nodes or drives can fail without materially impacting data integrity or access performance. The object approach also effectively parallelizes access to larger objects, since a number of nodes will all be transferring pieces of the object at the same time.

There are now a good number of software-only vendors today, all of which are installable on a wide variety of COTS hardware platforms. This includes the popular Ceph open source solution, backed by Red Hat. The combination of any of these software stacks and low-cost COTS gear makes object stores attractive on a price-per-terabyte basis, compared to traditional proprietary NAS or SAN gear.

Object storage is evolving to absorb the other storage models by offering a “universal storage” model where object, file and block access portals all talk to the same pool of raw object storage.  Likely, universal storage will deploy as object storage, with the other two access modes being used to create a file or block secondary storage to say all-flash arrays or filers. In the long term, universal storage looks to be the converging solution for the whole industry.

This trend is enhanced by the growth of software-defined storage (SDS). Object stores all run natively in a COTS standard server engine, which means the transition from software built onto an appliance to software virtualized into the instance pool is in most cases trivial. This is most definitely not the case for older proprietary NAS or SAN code. For object stores, SDS makes it possible to scale services such as compression and deduplication easily. It also opens up rich services such as data indexing.

Continue on to get up to speed on object storage and learn how it’s shaking up enterprise storage.

(Image: Kitch Bain/Shutterstock)



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