Everything you need to know about object storage

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 1

SNAPSHOT 1

Need for more

capacity driving

most NAS purchases

EDITOR’S NOTE / CASTAGNA

Data storage

growing faster than

capacity … for now

CLOUD BACKUP

Tap the cloud to

ease backup burdens

STORAGE REVOLUTION / TOIGO

Hyper-consolidated

is the future

MAY 2016, VOL. 15, NO. 3

SNAPSHOT 2

Users favor venerable

NFS and 10 Gig for

new NAS systems

HOT SPOTS / SINCLAIR

When simple storage

isn’t so simple

HADOOP

Big data requires

big storage

READ-WRITE / MATCHETT

The sun rises on

transformational

cloud storage services

MANAGING THE INFORMATION THAT DRIVES THE ENTERPRISE

Everything you need to know about object storage

Object storage is moving out of the cloud to

nudge NAS out of enterprise file storage

STORAGE

STORAGE • MAY 2016 3

JUST ABOUT A year ago, I got all jazzed about a new disco.-

ery that could turn DNA into a storage medium. That was

pretty cool—the stuff of life getting turned into the equi.-

alent of a 21st century floppy disk, but with a lot more

capacity and e.en more intriguing scenarios. Scientists

speculated that a mere gram of DNA could potentially

hold 2 TB of data. Not bad for a medium that until now

only handled mundane tasks like determining the color

of our eyes and whether or not we were going to lose our

hair e.entually.

Now the lab coat set has come up with another in-

genious alternati.e to boring old magnetic data storage

media or stolid solid state. In news reported by a number

of sources, including Research and Markets, the arri.al

of “quartz coin data storage” has been widely heralded.

Not to be confused with bitcoins, each of these coins

(de.eloped at the Uni.ersity of Southampton in the UK)

is said to be capable of holding 360 TB of data on a disk

that appears to be only a bit larger than a quarter. That’s a

lot of data in a small space, and it makes that 32 GB USB

flash dri.e on your keychain seem pretty puny.

According to the Research and Markets press release,

here’s how they did it:

“ The femtosecond laser encodes information in five

dimensions. The first three dimensions of X, Y and Z

denote the nano-grating’s location within the coin, while

the 4th and 5th dimension exist on the birefringence

patterns of the nano-gratings.”

Well, sure, if you’re going to resort to using birefrin-

gence patterns and nanogratings, then that kind of capac-

ity in that small of a space is no surprise. (Confession: I

ha.e no idea what birefringence patterns and nanograt-

ings are.)

All those terabytes on a small coin are pretty impres-

si.e, but what’s e.en more staggering is the predicted life

expectancy of this form of data storage media: 13.8 billion

years. That’s about the current age of the uni.erse! Sure,

that number is theoretical, but e.en if it’s only a measly

one billion years, I’m impressed. I just hope I’m around to

check on the accuracy of that forecast.

EDITOR’S LETTER

RICH CASTAGNA

Data storage growing faster than capacityFuture storage media may store

petabytes for eons, but the capacity

battle is happening now.

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 4

If you’re starting to think about reconfiguring your

data center to house a lot of little quartz coins, you might

want to slow down a bit. I don’t expect WD or Seagate are

likely to be stamping out quartz coin dri.es any time soon.

But maybe you don’t really need all that extra capacity

anyway.

HDD CAPACITIES ON THE DECLINE

Some market research firms ha.e reported that the total

hard disk capacity recently sold is down from pre.ious

periods. For example, TrendFocus noted that total HDD

capacity shipped declined sharply from about mid-2014 to

the middle of 2015. If you think that cloud and flash are

responsible for that dip and are filling the gap, that doesn’t

appear to be entirely true.

What’s more likely is that storage managers are finally

trying to get the upper hand in the struggle to control data

storage media capacity. And with new capacity require-

ments looming with big data analytics and IoT apps, it’s

kind of a now-or-ne.er proposition: Take control or take

co.er.

Our storage purchasing sur.eys re.eal that storage

buyers ha.e been better at planning ahead o.er the past

six or se.en years by buying bigger arrays and then filling

in with additional dri.es as needed. Similarly, while flash

is likely taking up all the slack of slumping hard disk dri.e

(HDD) sales, it’s also likely that solid state is affecting

storage buying habits. In the past, to squeeze out required

performance, a storage array might’.e been configured

with dozens of short-stroked 15K rpm disks, but now the

same—or better—performance can be achie.ed with far

fewer flash de.ices. So the net-net there is better perfor-

mance and a smaller capacity purchase.

Tape—you know, that type of data storage media that’s

been declared dead umpteen times in the past decade—

could also be making a difference due to its generous stor-

age capacities absorbing some of the capacity pre.iously

destined for disk.

CLEAR OUT ROTTEN STORAGE

Veritas’ recent (and oddly named) Global Databerg Report

declared that only 15% of the data a typical company has

stashed away on its storage systems is business-critical

stuff. And the report called 52% of the stored information

“dark data” because its .alue was unknown. The rest of the

data (33%) is described as “redundant, obsolete or tri.ial,”

or ROT—definitely one of the best acronyms I’.e seen in

some time.

I don’t really know how accurate Veritas’ numbers are,

but I bet that they’re at least in the ballpark for most com-

panies. In our most recent purchasing sur.ey, respondents

indicated that they manage an a.erage of 1.4 petabytes of

data on all forms of data storage media, including disks,

tape, flash, cloud, optical and whate.er else you can lay a

few bits and bytes on. If 33% of that is indeed ROT, that

means companies are paying for the care and feeding of

about half a petabyte of junk.

Perhaps data centers ha.e begun to get the ROT out.

Some of the newer and smarter storage systems make

the chore of finding and deleting rotten data easier by

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 5

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

pro.iding more intelligence about the stored information.

It’s also possible that tape, our fa.orite dead data storage

media, has been resurrected for archi.al purposes, and

both dark data and ROT ha.e found a final resting place.

The LTO Ultrium Consortium, which effecti.ely produces

the only tape game in town with its LTO format, reported

that .endors shipped 78,000 PB of compressed LTO tape

capacity in 2015.

In any e.ent, when you put the e.er-growing capacity

demands and the apparent downturn in disk capacity sales

side by side, you ha.e to conclude that we’re all getting at

least a little better at what we sa.e and what we dump. And

we’d better get used to it—it looks like selecti.e sa.ing will

be the way of life if we want to sur.i.e the imminent big

data/IoT data deluge. n

RICH CASTAGNA is TechTarget’s VP of Editorial.

STORAGE • MAY 2016 6

MANY RECENT CONVERSATIONS with IT folks confirm and

reinforce the trend away from the industry darling “hy-

per-con.erged infrastructure” (and, by extension, hy-

per-con.erged appliances) meme of the past 18 months

or so toward something best described as “hyper-consoli-

dation.” A friend of mine in Frankfurt, Germany—Chris-

tian Marczinke, .ice president of solution architecture

at DataCore Software—coined the term (to gi.e credit

where credit is due). But I first heard about hyper-con-

solidation when I inter.iewed IBM executi.es at the IBM

Interconnect 2016 conference in Las Vegas in February.

At that e.ent, IBM introduced its latest mainframe, the

z13s, which is designed to host not only the legacy data-

bases that are the mainstay of mainframe computing, but

also so-called “systems of insight” (read: big data analytics

systems) traditionally deployed on sprawling, mostly Li-

nux-based cluster farms in which indi.idual nodes consist

of a ser.er and some internal or locally-attached storage.

IBM made a pretty compelling case that all of those x86

ser.ers could be consolidated into VMs (or KVMs) run-

ning on its new big iron platform.

Through the combination of a lower cost and more

Linux-friendly mainframe with the integration of lots of

open source technologies already belo.ed by big data- and

Internet of Things-philes, the resulting hyper-consoli-

dated infrastructure would cost companies less money to

operate than hyper-con.erged appliances and facilitate

their transition into the realm of hybrid clouds. But it

would do all of this without the unknowns and insecurities

that typically accompany cloudification.

DISAPPOINTMENTS OF CONSOLIDATIONS PAST

Back in 2005, leading analysts actually produced charts

suggesting that hyper.isor computing would enable such

high le.els of ser.er-infrastructure consolidation that by

2009 Capex spending would all but flat line, while signif-

icant Opex cost reductions would start to be seen in e.ery

data center. It ne.er happened.

Then 2009 came and went and Capex cost kept right

on growing, in part because leading hyper.isor .endors

STORAGE REVOLUTION

JON TOIGO

Hyper- consolidated is the futureIBM mainframe illuminates a path toward

more manageable and cost-efficient

hyper-consolidated IT infrastructure.

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 7

blamed legacy storage (SANs, NAS and so on) for subpar

.irtual machine performance, telling their users that they

needed to rip and replace all storage in fa.or of direct-at-

tached storage cobbles. O.er time, this idea morphed into

software-defined storage, which .endors also touted as

“new,” but was actually a re-.isitation of System-Managed

Storage from much earlier mainframe computing days.

After a few false starts, the industry productized SDS as

hyper-con.erged infrastructure (HCI) appliances. Since

then, the trade press has been filled with ad.ertorials

subsidized by ser.er .endors-qua-HCI appliance .endors

talking about their “HCI cluster nodes,” combining com-

modity ser.er and storage stuff with lots of hyper.isor

and SDS software licenses, as though they were the new

normal in agile data center architecture and the Lego™

building blocks of clouds. Only, deploying a bunch of little

ser.er nodes—as IBM’s z13s-play suggests—is not really

consolidating much of anything. Nor is it really reducing

much cost. E.en as impro.ements are made in orchestra-

tion and administration of such infrastructures, the result

has been a return to the isolated-island-of-data problem

that companies sought to address in the late 1990s with

SANs.

ENTER HYPER-CONSOLIDATION

One way to clean up this mess with hyper-con.erged

appliances is to return to the mainframe. IBM facilitates

this with a hardware platform, the z13s, rooted deeply in

multiprocessor architecture and engineered for applica-

tion multi-tenancy. And to make it palatable to Millen-

nials who don’t know symmetrical multiprocessing from

Shinola, they ha.e ladled on (in the form of an archi-

tecture called LinuxONE) support for all of the Linux

distributors and open source appde. tools, cloudware,

analytics engines and in-memory databases that they

could lay their hands on. The idea is to consolidate a lot of

x86 platform workloads and storage into the more robust,

reliable and secure compute and storage of the z Systems

ecosystem.

IBM in.estment protects the system through “elastic

pricing,” which means you can get your money back if not

satisfied after the first year. (Interestingly, the presenta-

tions I saw at the IBM conference pointed out that users

were realizing superior ROI to either cloud computing or

x86 computing models after only three years with IBM’s

mainframe platform.) All in all, though, it is clear that

IBM’s idea has a lot of appeal—both to legacy “systems of

record” managers (the o.erseers of traditional ERP, MRP,

CRM and other workloads who like the reliability and se-

curity of the mainframe) and the appde.ers and cloudies

who prize Agile de.elopment o.er all else.

BIG IRON ISN’T FOR EVERYONE

Now, you don’t need to use a mainframe to do hyper-con-

solidation. Not e.ery company has the skills on staff to

run a mainframe, or the coin to finance the acquisition

of big iron—with or without elastic pricing. Marczinke

notes, for instance, that his clients are simply looking for

real sa.ings from consolidation that hyper.isor .endors

promised but didn’t deli.er. He may be right.

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 8

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

They are just as interested in hyper-consolidation,

but want to remain in the aegis of the x86 hardware and

hyper.isor software technologies they’re more familiar

with. These folks need something else: not just a sprawl-

ing infrastructure comprising hyper-con.erged appliances

that are each a data silo with a particular hyper.isor .en-

dor’s moat and stockade surrounding their workload and

data. They want to embrace consolidated storage—call

it something other than SANs if you want—so it is less

costly to manage, and they want to use locally attached

storage where that works. But they want all of that to be

manageable from a single pane of glass by someone who

knows little or nothing about storage.

Thankfully, some cool things are coming down the

pike in the realm of hyper-consolidation. If I am reading

the tea lea.es correctly, what DataCore has already done

with Adapti.e Parallel I/O on indi.idual hyper-con.erged

appliances, for example, could .ery well be on its way to

becoming much more scalable, creating—.ia software—a

mechanism to deli.er application performance and

latency reduction at the cluster level. Think of it as “no-

stall analytics for the rest of us.” Ultimately, this kind of

hyper-consolidation may be a winner across a .ery broad

swath of organizations that aren’t willing to outsource

their futures to the cloud and can’t stretch budgets to em-

brace IBM’s most excellent z Systems platform. n

JON WILLIAM TOIGO is a 30-year IT veteran, CEO and managing

principal of Toigo Partners International, and chairman of the Data

Management Institute.

STORAGE • MAY 2016 9

OBJECT STORAGE

What you need to know about object storage

A mainstay of cloud services, object storage is nudging NAS out of enterprise file storage.

BY JACOB GSOEDL

OBJECT STORAGE IS the latest alternati.e to traditional file-

based storage, offering greater scalability and (potentially)

better data management with its extended metadata. Until

recently, howe.er, object storage has largely been a niche

technology for enterprises while simultaneously becom-

ing one of the basic underpinnings of cloud storage.

Rapid data growth, the proliferation of big data lakes in

the enterprise, an increased demand for pri.ate and hybrid

cloud storage and a growing need for programmable and

scalable storage infrastructure are pulling object storage

technology from its niche existence to the mainstream as

a sound alternati.e to file-based storage.

An expanding list of object storage products, from both

major storage .endors and startups, is another indication

of object storage’s increasing rele.ance. Moreo.er, object

storage technology is reaching into network attached

storage (NAS) use cases, with some object storage .endors

positioning their products as .iable network attached

storage alternati.es.

To accomplish the lofty goal of o.ercoming the limita-

tions of traditional file- and block-le.el storage systems

to reliably and cost-effecti.ely support massi.e amounts

of data, object storage systems focus on and break new

ground when it comes to scalability, resiliency, accessibil-

ity, security and manageability. Let’s examine how object

storage systems do this.

HOME

VASABII/FOTOLIA

STORAGE • MAY 2016 10

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

SCALABILITY IS KEY TO OBJECT STORAGE

Complexity is anathema to extreme scalability. Object

storage systems employ se.eral techniques that are simple

in nature but essential to achie.ing unprecedented le.els

of scale.

To start with, object storage systems are scale-out sys-

tems that scale capacity, processing and networking re-

sources horizontally by adding nodes. While some object

storage products implement self-contained multifunction

nodes that perform access, storage and control tasks in a

single node, others consist of specialized node types. For

instance, IBM Cle.ersafe, OpenStack Swift and Red Hat

Ceph Storage differentiate between access and storage

nodes; con.ersely, each node in Caringo Swarm 8 and

EMC Elastic Cloud Storage (ECS) performs all object

storage functions.

Unlike the hierarchical structure of file-le.el storage,

object storage systems are flat, with a single namespace

in which objects are addressed .ia unique object identi-

fiers, thereby enabling unprecedented scale. “With 1038

object IDs a.ailable per .ault, we support a yottabyte-scale

namespace, and with each object segmented into 4 MB

segments, our largest deployments today are north of

100 petabytes of capacity, and we are prepared to scale

to and beyond exabyte-le.el capacity,” according to Russ

Kennedy, IBM senior .ice president product strategy,

Cle.ersafe.

Furthermore, object storage .endors are quick to note

their systems substitute the locking requirements of file-

le.el storage to pre.ent multiple concurrent updates (with

.ersioning of objects on update) enabling capabilities like

rollback and undeleting of objects as well as the inherent

ability to access prior object .ersions. Finally, object stor-

age systems replace the limited and rigid file system attri-

butes of file-le.el storage with rich customizable metadata

that not only capture common object characteristics but

can also hold application-specific information.

OBJECT OFFERS GREATER RESILIENCY

Traditional block- and file-le.el storage systems are

stymied by fundamental limitations to support massi.e

capacity. A case in point is data protection. It’s simply un-

realistic to back up hundreds of petabytes of data. Object

systems are designed to not require backups; instead, they

store data with sufficient redundancy so that data is ne.er

lost, e.en while multiple components of the object storage

infrastructure are failing.

Keeping multiple replicas of objects is one way of

achie.ing this. On the downside, replication is capacity-

intensi.e. For instance, maintaining six replicas requires

six times the capacity of the protected data. As a result,

object storage systems support the more efficient erasure

coding data protection method in addition to replication.

In simple terms, erasure coding uses ad.anced math to

create additional information that allows for recreating

data from a subset of the original data, analogous to RAID

5’s ability to retrie.e the original data from the remaining

dri.es despite one failing dri.e. The degree of resiliency

is typically configurable in contemporary object storage

systems. The higher the le.el of resiliency, the more stor-

age is required.

STORAGE • MAY 2016 11

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

Erasure coding sa.es capacity but impacts performance,

especially if erasure coding is performed across geograph-

ically dispersed nodes. “Although we support geographic

erasure coding, performing erasure coding within a data

center, but using replication between data centers is often

the best capacity/performance tradeoff,” said Paul Turner,

chief marketing officer at Cloudian. With large objects

yielding the biggest erasure coding payback, some object

storage .endors recommend data protection policies

based on object size. EMC ECS uses erasure coding lo-

cally and replication between data centers, but combines

replication with data reduction, achie.ing an o.erall data

reduction ratio similar to that of geo-dispersed erasure

coding without the performance penalty of the latter.

Object-storage use cases

n Backup and archival: Object storage systems are

cost-effective, highly scalable backup and archival

platforms, especially if data needs to be available for

continuous access.

n Enterprise collaboration: Geographically distributed

object storage systems are used as collaboration

platforms where content is accessed and shared

across the globe.

n Storage as a service: Object storage powers private

and public clouds of enterprises and service providers.

n Content repositories: Used as content repositories

for images, videos and other content accessed

through applications or via file system protocols.

n Log storage: Used to capture massive amounts of

log data generated by devices and applications,

ingested into the object store via a message broker

like Apache Kafka.

n Big data: Several object storage products offer cer-

tified S3 Hadoop Distributed File System interfaces

that allow Hadoop to directly access data on the

object store.

n Content distribution network: Used to globally dis-

tribute content like movies using policies to govern

access with features like automatic object deletion

based on expiration dates.

n Network Attached Storage (NAS): Used in lieu of

dedicated NAS systems, especially if there is another

use case that requires an object storage system. –J.G.

STORAGE • MAY 2016 12

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

The ability to detect and, if possible, correct object stor-

age issues is pertinent for a large, geographically dispersed

storage system. Continuous monitoring of storage nodes,

automatic relocation of affected data, and the ability to

self-heal and self-correct without human inter.ention

are critical capabilities to pre.ent data loss and ensure

continuous a.ailability.

STANDARDS-BASED ACCESSIBILITY

Object storage is accessed .ia a HTTP RESTful API to

perform the .arious storage functions, with each product

implementing its own proprietary APIs. All object storage

products also support the Amazon Simple Storage Ser.ice

(S3) API, which has become the de facto object storage

API standard—with by far the largest number of appli-

cations using it. It also has extensi.e and beyond simple

PUT, GET and DELETE operations and supports complex

storage operations.

The one thing to be aware of, though, is that most object

storage .endors only support an S3 API subset, and under-

standing the S3 API implementation limitations is critical

to ensure wide application support. Besides Amazon S3,

most object storage .endors also support the OpenStack

Swift API.

File system protocol support is common in object

storage systems, but implementations .ary by product.

For instance, EMC ECS has geo-distributed acti.e/acti.e

NFS support; and with ECS’ consistency support, it’s a

pretty strong geo-distributed NAS product. Scality claims

EMC Isilon-le.el NAS performance, and the NetApp

StorageGRID Webscale now offers protocol duality by

ha.ing a one-to-one relationship between objects and

files.

Other object storage products pro.ide file system

support through their own or third-party cloud storage

gateways like the ones offered by A.ere, CTERA Networks,

Nasuni and Panzura. Both Caringo Swarm and EMC

ECS offer Hadoop HDFS interfaces, allowing Hadoop to

directly access data in their object stores. HGST Ampli-

data and Cloudian pro.ide S3-compliant connectors that

enable Apache Spark and Hadoop to use object storage as

a storage alternati.e to HDFS.

ENCRYPTION PROVIDES NEEDED SECURITY

A common use case of an object storage product by ser-

.ice pro.iders is public cloud storage. Although at-rest

and in-transit encryption are a good practice for all use

cases, encryption is a must for public cloud storage. The

majority of object storage products support both at-rest

and in-transit encryption, using a low-touch at-rest en-

cryption approach where encryption keys are generated

dynamically and stored in the .icinity of encrypted objects

without the need for a separate key management system.

Cloudian HyperStore and HGST Amplidata support

client-managed encryption keys in addition to ser.er-side

managed encryption keys, gi.ing cloud ser.ice pro.iders

an option to allow their customers to manage their own

keys. Caringo Swarm, the DDN WOS Object Storage

platform and Scality RING currently don’t support at-rest

(Continued on page 14)

STORAGE • MAY 2016 13

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

Leading object storage productsPRODUCT DELIVERY OPTIONS NOTABLE FACTS

Caringo Swarm 8 Software-defined; delivered software-

only to run on commodity hardware.

n Out-of-box integration with Elasticsearch for fast object

search

IBM Cleversafe Software-defined; delivered software-

only to run on certified hardware.

n Multi-tiered architecture with no centralized serversn Extreme scalability enabled by peer-to-peer communication

of storage nodes

Cloudian

HyperStore

Software-defined; delivered as turnkey

integrated appliance or software-only

to run on commodity hardware.

n Stores metadata with objects, but also in a distributed

NoSQL Cassandra database for speed

DDN WOS Object

Storage platform

Software-defined; delivered as turnkey

integrated appliance or software-only

to run on commodity hardware.

n Configurations start as small as one noden Able to scale to hundreds of petabytes

EMC Elastic Cloud

Storage (ECS)

Software-defined; delivered as turnkey

integrated appliance or software-only

to run on commodity hardware.

n Features highly efficient strong consistency on access

of geo-distributed objectsn Designed with geo-distribution in mind

Hitachi Content

Platform (HCP)

Software-defined; delivered as turnkey

integrated appliance or software-only

to run on commodity hardware or as

a managed service hosted by HDS.

n Extreme density with a single cluster able to support up

to 800 million objects and 497 PB of addressable capacity n An integrated portfolio: HCP cloud storage; HCP Anywhere

File Sync & Share; Hitachi Data Ingestor (HDI) for remote

and branch offices

HGST Amplidata Software-defined; delivered

as a turnkey rack-level system.

n Uses HGST Helium filled hard drives to maximize power

efficiency, reliability and capacity

NetApp Storage-

GRID Webscale

Software-defined; delivered as software

appliance or turnkey integrated appliance.

n Stores metadata, including the physical location of objects,

in a distributed NoSQL Cassandra database

Red Hat Ceph Software-defined; delivered software-

only to run on commodity hardware.

n Based on the open-source Reliable Autonomic Distributed

Object Store (RADOS) n Features strong consistency on write

Scality RING Software-defined; delivered software-

only to run on commodity hardware.

n Stores metadata in a custom-developed distributed databasen Claims EMC Isilon performance if used as NAS

SwiftStack Object

Storage System

Software-defined; delivered software-

only to run on commodity hardware.

n Based on OpenStack Swiftn Enterprise offering of Swift with cluster and management

tools and 24-7 support.

STORAGE • MAY 2016 14

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

encryption, relying on application-based encryption of

data before it’s written to the object store.

LDAP and AD authentication support of users access-

ing the object store are common in contemporary object

storage systems. Support of AWS .2 or .4 authentication

to pro.ide access to .aults—and objects within .aults—is

less common and should be an e.aluation criterion when

selecting an object storage system.

OBJECT STORAGE MINIMIZES MANAGEMENT

Object storage systems are designed to minimize human

storage administration through automation, policy en-

gines and self-correcting capabilities. “The Cle.ersafe

system enables storage administrators to handle 15 times

the storage capacity of traditional storage systems,” claims

Kennedy.

Object storage systems are designed for zero downtime,

and all administration tasks can be performed without ser-

.ice disruption—from upgrades, hardware maintenance

and refreshes to adding capacity and changing data cen-

ters. Policy engines enable the automation of object stor-

age beha.ior, such as when to use replication .s. erasure

coding, under what circumstances to change the number

of replicas to support usage spikes, and what data centers

to store objects in based on associated metadata.

While commercial object storage products typi-

cally pro.ide management tools, technical support and

professional ser.ices to deploy and keep object storage

systems humming, the open-source OpenStack Swift

product demands a higher degree of self-reliance. For

companies that don’t ha.e the internal resources to deploy

and manage OpenStack Swift, SwiftStack sells an enter-

prise offering of Swift with cluster and management tools,

enterprise integration and 24-7 support.

TAKE AWAY

Without question, object storage systems are on the rise.

Their ability to scale and accessibility .ia APIs makes them

suitable in use cases where traditional storage systems

simply can’t compete. They’re also increasingly becom-

ing a NAS alternati.e, with some object storage .endors

claiming parity with NAS systems.

With a growing list of object storage products, choosing

an object storage system becomes increasingly challeng-

ing, howe.er. O.erall features and ensuring that your use

cases are supported, cost and .endor .iability are primary

decision criteria when in.estigating object storage sys-

tems. Still a relati.ely new technology with capabilities

.arying and in flux, reference checks and (if possible)

performing proof-of-concept testing are highly ad.is-

able before finalizing your object storage product selec-

tion. n

JACOB N. GSOEDL is a freelance writer and corporate VP of IT Business Solutions. He can be reached at [email protected].

(Continued from page 12)

STORAGE • MAY 2016 15

Snapshot 1Need for more capacity driving most NAS purchases

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 15

D Most critical features needed with new NAS

156

D Top drivers for new NAS purchase*

*UP TO THREE RESPONSES PERMITTED; SOURCE: TECHTARGET RESEARCH

*UP TO TWO RESPONSES PERMITTED; SOURCE: TECHTARGET RESEARCH

73% Need more capacity

30% Improve performance

25% Replacing existing NAS

20% New storage for new/specific app

14% Use as backup target

10% Better support virtual servers

8% Use as archive repository

5% Adding a new storage tier

3% For branch office(s)

48% File system size

37% Clustering support

32% Performance and capacity scale separately

22% Support for both NFS and CIFS (SMB 3.0)

10% Support for 10Gig Ethernet

D The average capacity of planned

new NAS system, in terabytes

STORAGE • MAY 2016 16

DATA IS THE lifeblood of most businesses today. And yet

the job of backing up that data is probably one of the least

lo.ed but most important processes in IT.

Few organizations could sur.i.e without the email and

producti.ity tools they use e.ery day, not to mention the

data (both current and archi.ed) these applications gener-

ate. And, at the other end of the spectrum, entire business

sectors, such as finance, couldn’t operate without huge

IT infrastructures and the .olumes of data they contain.

This makes it essential to implement a data protection

plan, that includes putting into place a reliable process

for backing up that data.

The public cloud and, in particular, cloud storage pro-

.ide organizations with a huge opportunity to implement

scalable, manageable and dependable backups. These

cloud backup options—such as Amazon Web Ser.ices

(AWS), Microsoft Azure and Google Cloud Platform—ef-

fecti.ely offer unlimited storage capacity at the end of a

network, with no need to understand how the supporting

infrastructure is constructed, managed or upgraded.

Public cloud .endors ha.e also introduced multiple

tiers of storage into their products to stay competiti.e.

AWS, for example, offers three le.els of storage (Standard,

Infrequent Access and Glacier) each of which deli.ers

different ser.ice le.els and price points. Google’s public

cloud mirrors AWS offerings with its Standard, Nearline

CLOUD BACKUP

Tap the cloud to ease backup

burdens Implement scalable and dependable

cloud backups for data, applications

and virtual machines.

BY CHRIS EVANS

ISTOCK

HOME

STORAGE • MAY 2016 17

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

and Durable Reduced A.ailability storage tiers.

There’s plenty of raw infrastructure a.ailable to store

your backup data. The question to ask now is what data

should be stored in the cloud and what cloud backup op-

tions do you use to back it up?

WHERE APPLICATIONS RUN MATTERS

To determine what data to store in the cloud and how to

back it up, we need to first see how IT deploys applica-

tions. Nowadays, businesses can run applications from

four main areas:

1. On premises (including private cloud). This happens

when running applications within a pri.ate data center

managed by local IT teams. Systems are built on internal

infrastructure and historically ha.e been backed up using

similar infrastructure within the data center, replicating

data to another location or taking backups off-site with

remo.able media.

2. Co-located. Rather than sit in a customer’s data center,

physical rack space is rented at a co-location facility that

manages the en.ironmental aspects of the data center,

while the customer continues to own the ser.er hard-

ware. Co-location pro.ides an opportunity for third-party

businesses to offer ser.ices like backup that are deployed

in the same co-location facility. This offloads the work of

backup but deli.ers low latency and high-throughput con-

necti.ity to backup infrastructure because of its physical

proximity.

3. Public cloud. The public cloud can be used to deploy .ir-

tual ser.ers and applications without businesses owning

or managing any of the underlying hardware. Infrastruc-

ture as a ser.ice (IaaS) .endors won’t pro.ide backup

capabilities outside of the requirement to return failing

systems back to normal operation, howe.er. So if a ser.er

crashes or data is lost, the IaaS .endor will simply return

the system to the pre.ious state of operation.

4. Public cloud. Platform as a ser.ice (PaaS) and software

as a ser.ice (SaaS) ha.e been widely adopted for the most

easily packaged and transferrable ser.ices, such as email

(e.g., Office 365), and applications, such as CRM (e.g.,

Salesforce). PaaS and SaaS offerings operate in a similar

way to IaaS, in that the platform pro.ider ensures systems

are always up and running with the latest .ersion of appli-

cations and data. They won’t directly pro.ide the ability

to reco.er historical data (e.g., when a user inad.ertently

deletes .ital account records), howe.er.

BACKUP OPTIONS FOR THE PUBLIC CLOUD

Organizations ha.e a number of choices among cloud

backup options that take ad.antage of public cloud stor-

age, including:

n Back up directly to the public cloud. Write data directly to

AWS Simple Storage Ser.ice (S3), Azure, Google or one

of many other cloud infrastructure pro.iders.

n Back-up-to-a-service provider. Write data to a ser.ice

STORAGE • MAY 2016 18

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

pro.ider offering backup ser.ices in a managed data

center.

n Implement disaster recovery as a service (DRaaS). A

number of .endors offer DR ser.ices that manage the

backup and restore process directly, focusing on the

application/.irtual machine rather than

just data. These DRaaS offerings also

work with PaaS/SaaS applications to

secure data already stored in the public

cloud.

Existing backup software pro.iders

ha.e extended their products to take

ad.antage of cloud storage as a nati.e

backup target. Veritas (formerly part of

Symantec) updated NetBackup to .er-

sion 7.7.1 toward the end of 2015, extend-

ing AWS S3 support to co.er the Standard

- Infrequent Access (IA) tier. (Version 7.7

originally introduced a cloud connector

feature with the ability to write directly to S3.)

The Comm.ault Data platform (formally called Sim-

pana) nati.ely supports all of the major public cloud

pro.iders and a range of object store .endors—including

Caringo and DataDirect Networks. It also supports an

extended set of .endors through standardization on the

S3 protocol, highlighting how S3 as a standard is being

used to pro.ide interoperability between object stores and

backup platforms, e.en if those systems are not running

in the public cloud.

A number of storage .endors ha.e also started to

support nati.e S3 backups from within their storage plat-

forms. SolidFire introduced the ability to archi.e snap-

shots to S3 or other SWIFT-compatible object stores as

part of the release of its Element OS Version 6 in March

2014. Zadara Storage, which offers a Virtual Pri.ate Stor-

age Array (VPSA) either on customer

premises or deployed at a co-location site,

pro.ides S3 support to archi.e snapshots

that can either be restored to Amazon’s

Elastic Block Store (EBS) ser.ice or any

other .endor’s storage hardware.

One word of caution when deciding to

use public cloud storage: Data written to

S3 and other ser.ices won’t be dedupli-

cated by the cloud pro.ider to reduce the

amount of space consumed by the user

(although they may deduplicate behind

the scenes). This means data must be

deduplicated before being written to the

cloud if that feature is not built into a

backup product.

One option to o.ercome this issue is to use software

such as that from StorReduce. Its cloud-based .irtual

appliance deduplicates S3 data, storing only the unique

data on the customer’s S3 account. (You can write to

StorReduce as the target in real time and it will write to

S3 in real time.) This significantly reduces the amount of

data stored on S3, which translates to cost sa.ings, both

in data stored and the transfer costs for reading and writ-

ing to S3 itself.

Cloud backup is no

longer simply about

shipping data to cheap

storage locations. To-

day, entire applications

can be migrated to, run

from and backed up to

and within public cloud

infrastructure.

STORAGE • MAY 2016 19

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

OPENING THE DOOR TO MSP AND SAAS BACKUPS

Managed ser.ice pro.iders (MSP) offer backup ser.ices

that take ad.antage of co-location facilities to offer cloud

backup options. If IT is already using hosting ser.ices

from companies such as Equinix, then backups can be

performed within the data center across the high-speed

network implemented by the hosting

company, rather than going out onto the

public Internet.

A number of software .endors, includ-

ing Asigra and Zerto, deli.er .ersions

of their products specifically designed

so that MSPs can deli.er a white-label

backup platform to their customers. The

benefit of using a ser.ice pro.ider for

backup is in the security of keeping data

within the MSP’s facilities. That way,

data doesn’t ha.e to tra.erse the public

Internet, which may resol.e issues of

compliance for some organizations. MSPs can also deli.er

“.alue-added” ser.ices that let customers run applications

in DR mode if primary systems fail.

SaaS, meanwhile, has allowed many IT shops to out-

source common applications to the public cloud—most

notably email, customer relationship management and

collaboration tools. While SaaS remo.es the need to

manage infrastructure and applications, it doesn’t fully

pro.ide data management capabilities. A SaaS pro.ider

will, for example, reco.er data from hardware or applica-

tion failure but not from common user errors such as the

accidental deletion of files or emails.

Products such as Spanning (acquired by EMC in 2014)

and Backupify (acquired by Datto the same year) enable

organizations to back up SaaS data. Pricing is typically

calculated on a per-user-per-month basis, which has to be

added into the o.erall cost of using a SaaS.

WHAT TO BACK UP?

THAT IS THE QUESTION

An important consideration when exam-

ining cloud backup options is deciding

what exactly to back up. It is possible

to back up only application data or an

entire .irtual machine, for example. The

ad.antage of a VM backup is that it makes

it possible to restart an application in

the cloud in the e.ent of a disaster at the

primary site. This also means IT doesn’t

need to ha.e specific DR hardware

and can instead operate applications from within the

cloud.

Datto is an example of a .endor that pro.ides customers

with the ability to run applications in DR mode in a cloud.

It offers a number of appliances that back up VMs locally,

replicating them to the pri.ate cloud Datto purpose-built

to allow customers to failo.er their applications in the

e.ent of a disaster.

Dru.a pro.ides a similar ser.ice with Phoenix DRaaS,

where entire applications can be backed up to the cloud

(through the replication of VM snapshots) and restarted

within AWS. The Dru.a application manages issues like

The S3 API provides a

common standard that

allows backup applica-

tions to write data to

both object storage and

public cloud providers

STORAGE • MAY 2016 20

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

IP address changes that need to be put in place as the ap-

plication is mo.ed to run in a different network.

CLOUD BACKUPS: TRADITIONAL VS. APPLIANCE

Traditional backup software applications ha.e been

modified to write directly to the cloud,

typically using standard protocols like

Amazon’s S3 API. In this instance, the

application needs to perform any data

reduction tasks like deduplication before

pushing the data out, as stored data is

charged per terabyte.

By comparison, application gateways

can be used to cache data as it is being

written to the cloud storage. The appli-

ance can then perform deduplication

and also cache data locally, allowing for

quicker restores from backup where needed. Typically,

the majority of restores occur within the first few days of

a backup being taken.

Traditional .s. appliance-based backups are import-

ant to consider because the public cloud is increasingly

becoming a practical target for data backups. The effec-

ti.e, limitless scale of the cloud takes away many of the

operational headaches associated with managing backup

infrastructure.

Ob.iously, there is a tradeoff between

running backup locally and using cloud

as the target, particularly in managing

network bandwidth. Howe.er, with the

ability to mo.e entire .irtual machines

into the cloud and run them there in

disaster reco.ery mode, we could see a

serious decline in the use of traditional

backup applications as IT realizes it no

longer needs to build out dedicated di-

saster reco.ery facilities or suffer the

impractical nature of shipping physical

media off-site. n

CHRIS EVANS is an independent consultant with Langton Blue.

Public cloud takes

away the need for

many IT shops to build

and manage their own

DR site

STORAGE • MAY 2016 21

Snapshot 2Users favor venerable NFS and 10 GigE for new NAS systems

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 21

D Top five apps to be deployed on new NAS systems

D NFS is still the preferred protocol for new NAS

*MULTIPLE SELECTIONS ALLOWED; SOURCE: TECHTARGET RESEARCH

59% Database applications

39% Web and application serving

33% Support for virtual servers

24% Unstructured data (e.g., user shares)

14% Virtual desktop infrastructure

SOURCE: TECHTARGET RESEARCH

n n n n n n n n n n n n n n n n n n n n n n n n n

n n n n n n n n n n n n n n n n n n n n n n n n n

n n n n n n n n n n n n n n n n n n n n n n n n n

n n n n n n n n n n n n n n n n n n n n n n n n n

69% plan to use 10 Gbps Ethernet to hook up their new NAS

79% NFS

43% CIFS

21%

SMB 3.0

STORAGE • MAY 2016 22

IT'S COMMON FOR storage discussions to begin with a ref-

erence to data growth. The implied assumption is that

companies will want to capture and store all the data they

can for a growing list of analytics applications. Today, be-

cause the default policy for retaining stored data within

many enterprises is “sa.e e.erything fore.er,” many orga-

nizations are regularly accumulating multiple petabytes

of data.

Despite what you might think about the commoditiza-

tion of storage, there is a cost to storing all of this data. So

why do it? Because executi.es today realize that data has

intrinsic .alue due to ad.ances in data analytics. In fact,

that data can be monetized. There’s also an understand-

ing at the executi.e le.el that the .alue of owning data is

increasing while the .alue of owning IT infrastructure is

decreasing.

Hadoop Distributed File System (HDFS) is fast becom-

ing the go-to tool enterprise storage users are adopting to

tackle the big data problem, and here’s a closer look as to

how it became the primary option.

WHERE TO PUT ALL THAT DATA?

Traditional enterprise storage platforms—disk arrays and

tape siloes—aren’t up to the task of storing all of the data.

Data center arrays are too costly for the data .olumes

HADOOP

Big data requires big storage

Hadoop deployments evolve thanks to enterprise

storage vendors and the Apache community.

BY JOHN WEBSTER

TOTALLYPIC/FOTOLIA

HOME

STORAGE • MAY 2016 23

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

en.isioned, and tape, while appropriate for large .olumes

at low cost, elongates data retrie.al. The repository sought

by enterprises today is often called the big data lake, and

the most common instantiation of these repositories is

Hadoop.

Originated at the Internet data centers of Google and

Yahoo, Hadoop was designed to deli.er high-analytic

performance coupled with large-scale storage at low cost.

There is a chasm between large Internet data centers

and enterprise data centers that’s defined by differences

in management style, spending priorities, compliance and

risk-a.oidance profiles, howe.er. As a result, the Hadoop

Distributed File System was not originally designed for

long-term data persistence. The assumption was data

would be loaded into a distributed cluster for MapReduce

batch processing jobs and then unloaded—a process that

would be repeated for successi.e jobs.

Nowadays, enterprises not only want to run successi.e

MapReduce jobs, they want to build multiple applications

that, for example, con.erge analytics with the data gen-

erated by online transaction processing (OLTP) on top

of the Hadoop Distributed File System. Common storage

for multiple types of analytics users is needed as well (see

Figure 1, Hadoop’s multiple application en.ironment

supported by YARN and the Hadoop Distributed File

System). Some of the more popular applications include

Apache HBase for online transaction processing, and

Apache Spark and Storm for data streaming as well as

real-time analytics. To do this, data needs to be persisted,

protected and secured for multiple user groups and for

long periods of time.

FILLING THE HADOOP STORAGE GAP

Current .ersions of Hadoop Distributed File System ha.e

storage management features and functions consistent

with persisting data, and the Apache open-source com-

munity works continuously to impro.e HDFS to make it

more compatible with enterprise production data centers.

Some important features are still missing, howe.er. So

the challenge for administrators is to determine whether

or not the HDFS storage layer can in fact ser.e as an

acceptable data-preser.ation foundation for the Hadoop

analytics platform and its growing list of applications and

users. With multiple Apache community projects taking

the attention of de.elopers, users are often kept waiting

for production-ready Hadoop storage functionality in fu-

ture releases. The current list of Hadoop storage gaps to

be closed includes:

n Inefficient and inadequate data protection and DR

capabilities. HDFS relies on the creation of replicated

data copies (usually three) at ingest to reco.er from disk

failures, data loss scenarios, loss of connecti.ity and re-

lated outages. While this process does allow a cluster to

tolerate disk failure and replacement without an outage,

it still doesn’t totally co.er data loss scenarios that include

data corruption.

In a recent study, researchers at North Carolina State

Uni.ersity found that while Hadoop pro.ides fault toler-

ance, “data corruptions still seriously affect the integrity,

performance, and a.ailability of Hadoop systems.” This

process also makes for .ery inefficient use of storage

media—a critical concern when users wish to retain data

STORAGE • MAY 2016 24

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

in the Hadoop cluster for up to se.en years, as may be

required for regulatory compliance reasons. The Apache

Hadoop de.elopment community is looking at imple-

menting erasure coding as a second “tier” for low-frequen-

cy-of- access data in a new .ersion of Hadoop Distributed

File System later this year.

HDFS also cannot replicate data synchronously be-

tween Hadoop clusters, a problem because synchronous

replication is critical for supporting production-le.el

DR operations. And while asynchronous replication is

supported, it’s open to the creation of file inconsistencies

across local/remote cluster replicas o.er time.

Example of Hadoop Analytics Environment StructureHadoop’s multiple application environment (MapReduce, SQL/NoSQL and in-memory analytics,

for example) supported by YARN (Yet Another Resource Negotiator) as a platform OS and

HDFS as persistent storage for all applications running above the HDFS storage layer.

SOURCE: HORTONWORKS AND EVALUATOR GROUP

Batch

Map-

Reduce

Multitenant Processing: YARN

(Hadoop Operating System)

Storage: HDFS

(Hadoop Distributed File System)

SQL

Hive

Online

HBase

Accumulo

InMemory

Spark

Others

STORAGE • MAY 2016 25

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

n Inability to disaggregate storage from compute re-

sources. HDFS binds compute and storage together to

minimize the distance between processing and data for

performance at scale, resulting in some unintended conse-

quences for when HDFS is used as a long-term persistent

storage en.ironment. To add storage capacity in the form

of data nodes, an administrator has to add processing re-

sources as well, needed or not. And remember that 1 TB

of usable storage equates to 3 TB after copies are made.

Data in/out processes can take longer than actual query

process. One of the major ad.antages of using Hadoop for

analytics applications .s. traditional data warehouses lies

in its ability to run queries against .ery large .olumes of

unstructured data. This is often accomplished by copying

data from acti.e data stores to the big data lake, a process

that can be time-consuming and network resource-in-

tensi.e, depending on the amount of data. Perhaps more

critically from the standpoint of Hadoop in production,

this can lead to data inconsistencies, causing application

users to question whether or not they are querying a single

source of the truth.

ALTERNATIVE HADOOP ADD-ONS

AND STORAGE SYSTEMS

The Apache community often creates add-on projects to

address Hadoop deficiencies. Administrators can use the

Raft distributed consensus protocol to reco.er from

cluster failures without recomputation, and the DistCp

(distributed copy) tool for periodic synchronization of

clusters across WAN distances. Falcon, a feed process-

ing and management system, addresses data lifecycle and

management, and the Ranger framework centralizes se-

curity administration. These add-ons ha.e to be installed,

learned and managed as separate entities, and each has its

own lifecycle, requiring tracking and updating.

To address these issues, a growing list of administrators

ha.e begun to integrate data-center-grade storage systems

with Hadoop—ones that come with the required data

protection, integrity, security and go.ernance features

built-in. The list of “Hadoop-ready” storage systems in-

cludes EMC Isilon and EMC Elastic Cloud Storage (ECS),

Hitachi’s Hyper Scale-Out Platform, IBM Spectrum Scale

and NetApp’s Open Solution for Hadoop.

Let’s look at two of these external Hadoop storage sys-

tems in more detail to understand the potential .alue of

this alternate route.

EMC ELASTIC CLOUD STORAGE

ECS is a.ailable as a preconfigured hardware/software

appliance, or as software that can be loaded onto scale-

out racks of commodity ser.ers. It supports object storage

ser.ices as well as HDFS and NFS .3 file ser.ices. Object

access is supported .ia Amazon Simple Storage Ser.ice

(S3), Swift, OpenStack Keystone V3 and EMC Atmos

interfaces.

ECS uses Hadoop as a protocol rather than a file system

and requires the installation of code at the Hadoop cluster

le.el, and the ECS data ser.ice presents Hadoop cluster

nodes with Hadoop-compatible file system access to its

unstructured data. It supports both solid-state and hybrid

STORAGE • MAY 2016 26

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

hard dri.e storage embedded into ECS nodes, and scales

up to 3.8 PB in a single rack depending on user configu-

ration. Data and storage management functions include

snapshot, journaling and .ersioning, and ECS implements

erasure coding for data protection. All ECS data is erasure

coded except the index and metadata where ECS main-

tains three copies of the data.

Additional features of .alue in the context of enterprise

production-le.el Hadoop include:

n Consistent write performance for small and large file

sizes. Small file writes are aggregated and written as one

operation while parallel node processing is applied to

large file access.

n Multisite access and three-site support. ECS allows for

immediate data access from any ECS site in a multisite

cluster supported by strong consistency (applications

presented with the latest .ersion of data, regardless of

location and indexes across all locations synchronized).

ECS also supports primary, remote and secondary sites

across a single cluster, as well as asynchronous replication.

n Regulatory compliance. ECS allows administrators to

implement time-based data-retention policies. It supports

compliance standards such as SEC Rule 17a-4. EMC Cen-

tera CE+ lockdown and pri.ileged delete also supported.

n Search. Searches can be performed across user-defined

and system-le.el metadata. Indexed searching on key

.alue pairs is enabled with a user-written interface.

n Encryption. Inline data-at-rest encryption with auto-

mated key management where keys generated by ECS are

maintained within the system.

IBM SPECTRUM SCALE

IBM Spectrum Scale is a scalable (to multi-PB range),

high-performance storage system that can be nati.ely in-

tegrated with Hadoop (no cluster-le.el code required). It

implements a unified storage en.ironment, which means

support for both file and object-based data storage under

a single global namespace.

For data protection and security, Spectrum Scale of-

fers snapshots at the file system or set le.el, and backup

to an external storage target (backup appliance and/or

tape). Storage-based security features include data-at-rest

encryption and secure erase plus LDAP/AD for authenti-

cation. Synchronous and asynchronous data replication

at LAN, MAN and WAN distances with transactional

consistency is also a.ailable.

Spectrum Scale supports automated storage tiering

using flash for performance and multi-terabyte mechan-

ical disk for inexpensi.e capacity with automated, poli-

cy-dri.en data mo.ement between storage tiers. Tape is

a.ailable as an additional archi.al storage tier.

Policy-dri.en data compression can be implemented on

a per-file basis for an approximately 2x impro.ement in

storage efficiency and reduced processing load on Hadoop

cluster nodes. And, for mainframe users, Spectrum Scale

can be integrated with IBM z Systems, which often play

the role of remote data islands when it comes to Hadoop.

STORAGE • MAY 2016 27

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

A SPARK ON THE HORIZON

Apache Spark as a platform for big data analytics runs

MapReduce applications faster than Hadoop, but also like

Hadoop is a multi-application platform offering analysis

of streaming data. Spark’s more efficient code base and

in-memory processing architecture accelerate perfor-

mance while still le.eraging commodity hardware and

open-source code. Unlike Hadoop, Spark does not come

with its own persistent data storage layer, howe.er, so the

most common Spark implementations are on Hadoop

clusters using HDFS.

The growth of Spark is the result of growing interest

in stream processing and real-time analytics. Again,

Hadoop Distributed File System wasn’t originally con-

cei.ed to function as a persistent data store underpinning

streaming analytics application. Spark will make storage

performance tiering for Hadoop e.en more attracti.e, yet

another reason to consider marrying Hadoop with enter-

prise storage. n

JOHN WEBSTER is a senior partner and analyst at the Evaluator Group.

DELIVERING VALUE THROUGH inno.ation is the goal of many

of today’s technology de.elopment firms. Enterprise

storage systems are no different. While each new storage

product seeks to deli.er substantial benefits, some are

more tangible than others. One specific feature, so-called

“simplicity,” commonly finds itself on the wrong end of the

tangibility spectrum—too often deli.ering .ague or e.en

immaterial benefits to customers.

The use of the term “simple” in storage product market-

ing is so per.asi.e, its effecti.eness has been weakened.

In fact, I’.e yet to see an enterprise storage system (tra-

ditional storage arrays, mission-critical tier-one storage

monoliths, or e.en open-source storage software) that isn’t

marketed as “simple.” For a simplicity claim to be truly

effecti.e, a product needs to deli.er a measurable financial

benefit to businesses. Translating simplicity claims into

business impacts is often a challenge, howe.er. So I am

proposing a different way of thinking about simplicity.

TWO SIMPLE WAYS TO THINK OF SIMPLICITY

1. Look past the user interface: Most IT systems should

ha.e at least some form of graphical user interface, and

that interface should endea.or to make it easy to digest

all necessary information while reducing the number of

steps required to use the product effecti.ely. For the .ast

majority of enterprise storage systems, graphical interface

design has hit the law of diminishing returns, though. In

addition, the definition of a simple interface is relati.e to

the user. Reducing the number of steps from fi.e to two,

for example, should impro.e the ease of use, but not if the

user is already adept at those fi.e steps. And, for IT orga-

nizations that use scripting, these simplicity inno.ations

can pro.ide little to no benefit in e.eryday use. For these

en.ironments, consistency is usually more critical.

2. Efficiency is the new simplicity: Without impro.ing ef-

ficiency, notions of simplicity are meaningless. Reducing

the number of storage elements to be managed, deployed

or supported deli.ers far more tangible and impactful

benefits to the bottom line than simply reducing the

number of steps in an interface. Greater efficiency offers

HOT SPOTS

SCOTT SINCLAIR

When simple storage isn’t so simpleStorage simplicity is about the tangible

benefits that efficiency delivers.

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 28

STORAGE • MAY 2016 29

ob.ious benefits to capital costs, as less equipment trans-

lates directly to reduced capital expenses. Here, I focus on

operational expenditures, which can often be reduced in

one of two ways.

The first method is to reduce the number of storage

elements to manage (e.g., managing one large data pool

instead of dozens of small ones). The second method

reduces the number of physical components or systems

that need to be deployed and supported, such as deploy-

ing a single all-flash array to replace the performance of

dozens of shel.es of spinning dri.e media. Fortunately,

the storage industry is rife with efficiency-augmenting

inno.ations. The following are se.eral examples of newer

storage technologies that deli.er tangible simplicity ben-

efits through more efficient designs.

n Hyper-convergence: The ability to consolidate multiple

ser.ers, switches and enterprise storage systems into

only a few hyper-con.erged platforms reduces not only

the number of components IT is required to manage, it

decreases the number of physical systems as well. In this

way, hyper-con.erged .endors, such as Atlantis Hyper-

Scale, Nutanix and SimpliVity, simplify IT infrastructure

to deli.er measurable sa.ings.

n Solid-state storage: For transactional workloads, achie.-

ing high performance with spinning media requires large

quantities of spindles, increasing the number of storage

elements required to manage, deploy and protect. The

performance density of solid-state greatly reduces the

amount of equipment required, and therefore simplifies

infrastructure deployment and design. The net result is a

reduction in the cost of operations.

In addition to this performance density ad.antage,

multiple flash storage .endors offer additional efficiency

capabilities. All-flash enterprise storage systems, such as

EMC’s XtremIO, NetApp’s SolidFire and now Nimble’s

Adapti.e Flash, pro.ide scale-out architectures that re-

duce the number of management elements. While not

scale-out, Pure Storage, meanwhile, offers a modular

array that allows IT to expand performance across hard-

ware generations, reducing the demand for incremental

deployments.

n Scale-out file or object storage: For high-capacity work-

loads, scale-out file and object storage systems deli.er a

single, massi.ely scalable pool of storage, reducing the

number of storage elements to manage. Products from

.endors such as Caringo, IBM/Cle.ersafe, Cloudian,

EMC, HGST, Qumulo and Scality can significantly reduce

the number of file systems that organizations manage. In

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

FOR THE VAST MAJORITY OF ENTERPRISE STORAGE SYSTEMS, GRAPHICAL INTERFACE DESIGN HAS HIT THE LAW OF DIMINISHING RETURNS.

STORAGE • MAY 2016 30

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

addition, some of these products offer automatic multisite

resiliency, eliminating the need to manage dozens of rep-

lication processes.

n Software-defined storage (SDS): Abstracting storage

functionality from the underlying hardware can pro.ide

greater deployment flexibility and management consol-

idation for multiple heterogeneous storage elements.

Either of these features can allow for a more efficient

infrastructure design to impro.e simplicity and reduce

operational costs.

Some SDS offerings enable organizations to further

reduce the amount of storage infrastructure required.

DataCore with its Parallel I/O technology, for example,

claims to effecti.ely le.erage parallel processing to deli.er

greater performance from existing components, further

extending gains from efficiency.

TWO SIMPLE QUESTIONS WORTH ASKING

These are just a few examples of storage inno.ations that

are reducing operational costs by deli.ering greater effi-

ciency and simplicity. Ultimately, when a storage .endor

says its product is simple, look past the interface and ask

two questions:

1. Does the technology reduce the number of storage

elements you ha.e to manage? If yes, then it will help

reduce Opex.

2. Does it reduce the number of physical storage com-

ponents needed to be deployed and supported? If yes,

sa.ings will likely be e.en greater. n

SCOTT SINCLAIR is a storage analyst with Enterprise Strategy Group in Austin, Texas.

WE HAVE BEEN hearing about the ine.itable transition to

the cloud for IT infrastructure since before the turn of

the century. But, year after year, storage shops quickly

become focused on only that year’s prioritized initiati.es,

which tend to be mostly about keeping the lights on and

costs low. A true .ision-led shift to cloud-based storage

ser.ices requires explicit executi.e sponsorship from the

business side of an organization. But unless you cynically

count the creeping use of shadow IT as an actual strategic

directi.e to do better as an internal ser.ice pro.ider, what

gets asked of you is likely—and unfortunately—to per-

form only low-risk tactical deployments or incremental

upgrades.

Not exactly the stuff of business transformations.

Cloud adoption at a le.el for maximum business impact

requires big executi.e commitment. That amount of com-

mitment is, quite frankly, not easy to generate.

THE TRANSFORMATIONAL CLOUD

We all know cloud opportunities exist beyond a bit of cold

archi.e data storage here and there. These ser.ices not

only sa.e real money, but can also significantly realign IT

effort from just running infrastructure to increasing busi-

ness .alue. Yet most IT shops run too lean and mean, and

lack the skills, willpower or time to go off and actually risk

transitioning core workloads (or otherwise take ad.antage

of) to hybrid or public cloud-based storage ser.ices.

Instead, the pre.alent attitude is to passi.ely accept

that some hybrid cloud usage is ine.itably going to creep

in so organizations can check the cloud “box” on their in-

ternal score cards. Perhaps it’s a storage as a system (SaaS)

integration with on-premises apps, or maybe more cloud

storage as a target for backups and archi.e data emanating

from some future storage update project, and so on.

It is time for e.eryone to make bolder cloud mo.es.

Current market offerings are low-risk, easy to adopt and

pro.ide enough payback to help justify a larger transition

and commitment to cloud-based storage ser.ices for e.en

the most traditional organization. The key to success for

both IT and .endors is to find that one cloud proof-point

READ / WRITE

MIKE MATCHETT

The sun rises on transformational cloud storageStorage managers can seize the

day and help their companies take

advantage of the cloud.

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 31

STORAGE • MAY 2016 32

that is .isible enough to business stakeholders to moti.ate

and accelerate a larger cloud strategy.

Surprisingly, business-impacting cloud transformation

is where storage folks can demonstrate significant lead-

ership. Instead of storage lagging fi.e years behind other

domains (well, that’s the impression, isn’t it?), it can lead

the way. To be clear, I’m talking about le.eraging cloud

for more than just cold backup/archi.e tiering, special big

data projects or one-off Web 2.0 use cases. While the cloud

can certainly pro.ide all that, let’s look at some examples

of cloud-based storage ser.ices that hit directly at the

heart of daily business operations.

MAXIMUM IMPACT CLOUD SERVICES

First, let’s acknowledge that most businesses today depend

on file sync-and-share ser.ices. The more functional and

frictionless these products are to use, the more they get

used. It’s easy enough to go the SaaS route if that meets

all your needs and fits your budget structure, but I would

point out there are ultimately more affordable and easy-

to-deploy file sync-and-share ser.ices a.ailable.

Take CTERA 5.0, for example. With CTERA organiza-

tions can not only check the box for full IT-go.erned file

sync-and-share, they also get a host of other .irtual pri.ate

cloud capabilities for today’s increasingly distributed and

mobile businesses. If tier 1 performance is your main con-

cern with cloud storage, check ClearSky Data, which built

a “metro area” cloud deli.ering sub-millisecond latency.

Both CTERA and ClearSky Data’s ser.ices le.erage the

cloud for capacity and distribution, but ser.e important

data at the edge for top performance.

Another example of a business-impactful cloud-based

storage ser.ice is Ri.erbed’s SteelFusion, which offers a

related but different cloud storage approach than CTERA

and ClearSky Data for remote and back offices. By “pro-

jecting” data center storage out to remote locations using

world-class WAN optimization built into “edge hyper-con-

.erged” appliances, SteelFusion effecti.ely turns any

enterprise data center storage array into a pri.ate cloud-

like storage host supporting highly performant remote

(localized) processing.

THE CLOUD AND TRADITIONAL STORAGE

As an industry, we are starting to expect cloud tiering as

something arrays should nati.ely support. Old-school

.endors, meanwhile, are concerned about protecting ca-

pacity-based legacy storage re.enue streams—although

there is some change occurring there, too.

IBM and EMC, for instance, are each working to iron

out the kinks in how their traditional storage di.isions

work with their respecti.e clouds. And, as another ex-

ample, Microsoft Azure StorSimple—what we used to

think of as a simple storage appliance—has e.ol.ed into

something much more. With automatic backup and cloud

tiering (to Microsoft Azure) and a new .irtual StorSimple

option that can also run in Azure for in-cloud reco.ery,

small IT storage projects that at first only seem to be about

cheaper and better protected local shared storage, quickly

help justify and accelerate larger and more impactful busi-

ness-cloud transformations.

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

STORAGE • MAY 2016 33

Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

And lest you think cloud-based storage ser.ices are only

for mobile users and midrange storage, Oracle’s cloud can

now ser.ice mainframe storage data, too. That’s because

with Oracle’s recent StorageTek Virtual Storage Manager

System 7 release, mainframe managers can now do away

with tape and use Oracle Cloud as an enterprise main-

frame storage tier.

LIGHTNING STRIKES FOR CLOUD STORAGE SERVICES

The common benefit of all these cloud-based storage ser-

.ices is that while they may be initiated to sol.e practical

IT issues, once in place, they pro.e the wider .alue of the

cloud and excite business executi.es to champion further

cloud transition. While IT folks may ha.e to adapt to op-

erating cloud-style, the cloud enables IT to focus more on

addressing business-le.el technology needs rather than

just supporting infrastructure. If your company has talked

about the cloud, but not really gotten any momentum

around major cloud business transition efforts, now is a

good time to see if a simple storage refresh project can be

used to stimulate and pa.e the way to the cloud.

Bottom line: If you mo.e the data, the business mo.es,

too. Cloud success fundamentally transforms IT and

redefines the relationship between IT and business stake-

holders. n

MIKE MATCHETT is a senior analyst and consultant at Taneja Group.

STORAGE • MAY 2016 34

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Home

Castagna: Big

storage cooking

up in labs

Toigo: Hyper-

consolidation

the future

Object storage

squeezing out NAS

Snapshot 1:

Capacity driving

most NAS

purchases

Cloud alternatives

to local backup

Snapshot 2:

NFS and 10 Gig pre-

ferred for new NAS

Hadoop’s role in

big data storage

Sinclair: Simple

storage not so

simple

Matchett: Say hi

to the transforma-

tional cloud

About us

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