Considerations of Hadoop in the Enterprise

I am guilty. I have not been tendering this blog for quite a while now, but it feels good to be back. What have I been doing? Since leaving NetApp 2 months or so ago, I have been active in the scenes again. This time I am more aligned towards data analytics and its burgeoning impact on the storage networking segment.

I was intrigued by an article posted by a friend of mine in Facebook. The article (circa 2013) was titled “Never, ever do this to Hadoop”. It described the author’s gripe with the SAN bigots. I have encountered storage professionals who throw in the SAN solution every time, because that was all they know. NAS, to them, was like that old relative smelled of camphor oil and they avoid NAS like a plague. Similar DAS was frowned upon but how things have changed. The pendulum has swung back to DAS and new market segments such as VSANs and Hyper Converged platforms have been dominating the scene in the past 2 years. I highlighted this in my blog, “Praying to the Hypervisor God” almost 2 years ago.

I agree with the author, Andrew C. Oliver. The “locality” of resources is central to Hadoop’s performance.

Consider these 2 models:

moving-compute-storage

In the model on your left (Moving Data to Compute), the delivery process from Storage to Compute is HEAVY. That is because data has dependencies; data has gravity. However, if you consider the model on your right (Moving Compute to Data), delivering data processing to the storage layer is much lighter. Compute or data processing is transient, and the data in the compute layer is volatile. Once compute’s power is turned off, everything starts again from a clean slate, hence the volatile stage.

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Can CDMI emancipate an interoperable medical records cloud ecosystem?

PREFACE: This is just a thought, an idea. I am by no means an expert in this area. I have researched this to inspire a thought process of how we can bring together 2 disparate worlds of medical records and imaging with the emerging cloud services for healthcare.

Healthcare has been moving out of its archaic shell in the past few years, and digital healthcare technology and services are booming. And this movement is part of the digital transformation which could eventually lead to a secure and compliant distribution and collaboration of health data, medical imaging and electronic medical records (EMR).

It is a blessing that today’s medical imaging industry has been consolidated with the DICOM (Digital Imaging and Communications in Medicine) standard. DICOM dictates the how medical imaging information and pictures are used, stored, printed, transmitted and exchanged. It is also a communication protocol which runs over TCP/IP, and links up different service class providers (SCPs) and service class users (SCUs), and the backend systems such as PACS (Picture Archiving & Communications Systems) and RIS (Radiology Information Systems).

Another well accepted standard is HL7 (Health Level 7), a similar Layer 7, application-level communication protocol for transferring and exchanging clinical and administrative data.

The diagram below shows a self-contained ecosystem involving the front-end HIS (Hospital Information Systems), and the integration of healthcare, medical systems and other DICOM modalities.

Hospital Enterprise

(Picture courtesy of Meddiff Technologies)

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Don’t get too drunk on Hyper Converged

I hate the fact that I am bursting the big bubble brewing about Hyper Convergence (HC). I urge all to look past the hot air and hype frenzy that are going on, because in the end, the HC platforms have to be aligned and congruent to the organization’s data architecture and business plans.

The announcement of Gartner’s latest Magic Quadrant on Integrated Systems (read hyper convergence) has put Nutanix as the leader of the pack as of August 2015. Clearly, many of us get caught up because it is the “greatest feeling in the world”. However, this faux feeling is not reality because there are many factors that made the pack leaders in the Magic Quadrant (MQ).

Gartner MQ Integrated Systems Aug 2015

First of all, the MQ is about market perception. There is no doubt that the pack leaders in the Leaders Quadrant have earned their right to be there. Each company’s revenue, market share, gross margin, company’s profitability have helped put each as leaders in the pack. However, it is also measured by branding, marketing, market perception and acceptance and other intangible factors.

Secondly, VMware EVO: Rail has split the market when EMC has 3 HC solutions in VCE, ScaleIO and EVO: Rail. Cisco wanted to do their own HC piece in Whiptail (between the 2014 MQ and 2015 MQ reports), and closed down Whiptail when their new CEO came on board. NetApp chose EVO: Rail and also has the ever popular FlexPod. That is why you see that in this latest MQ report, NetApp and Cisco are interpreted independently whereas in last year’s report, it was Cisco/NetApp. Market forces changed, and perception changed.  Continue reading

The transcendence of Data Fabric

The Register wrote a damning piece about NetApp a few days ago. I felt it was irresponsible because this is akin to kicking a man when he’s down. It is easy to do that. The writer is clearly missing the forest for the trees. He was targeting NetApp’s Clustered Data ONTAP (cDOT) and missing the entire philosophy of NetApp’s mission and vision in Data Fabric.

I have always been a strong believer that you must treat Data like water. Just like what Jeff Goldblum famously quoted in Jurassic Park, “Life finds a way“, data as it moves through its lifecycle, will find its way into the cloud and back.

And every storage vendor today has a cloud story to tell. It is exciting to listen to everyone sharing their cloud story. Cloud makes sense when it addresses different workloads such as the sharing of folders across multiple devices, backup and archiving data to the cloud, tiering to the cloud, and the different cloud service models of IaaS, PaaS, SaaS and XaaS.

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Oops, excuse me but your silo is showing

It is the morning that the SNIA Global Steering Committee reporting session is starting soon. I am in the office extremely early waiting for my turn to share the happenings in SNIA Malaysia.

And of late, I have been getting a lot of calls to catch up on hot technologies, notably All Flash Storage arrays and hyper-converged infrastructure. Even though I am now working for Interica, a company that focuses on Oil & Gas exploration and production software, my free coffee sessions with folks from the IT side have not diminished. And I recalled a week back in mid-March where I had coffee overdose!

Flash storage and hyperconvergence are HOT! Despite the hypes and frenzies of both flash storage and hyperconvergence, I still believe that integrating either or, or both, still have an effect that many IT managers overlook. The effect is a data silo.

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The reverse wars – DAS vs NAS vs SAN

It has been quite an interesting 2 decades.

In the beginning (starting in the early to mid-90s), SAN (Storage Area Network) was the dominant architecture. DAS (Direct Attached Storage) was on the wane as the channel-like throughput of Fibre Channel protocol coupled by the million-device addressing of FC obliterated parallel SCSI, which was only able to handle 16 devices and throughput up to 80 (later on 160 and 320) MB/sec.

NAS, defined by CIFS/SMB and NFS protocols – was happily chugging along the 100 Mbit/sec network, and occasionally getting sucked into the arguments about why SAN was better than NAS. I was already heavily dipped into NFS, because I was pretty much a SunOS/Solaris bigot back then.

When I joined NetApp in Malaysia in 2000, that NAS-SAN wars were going on, waiting for me. NetApp (or Network Appliance as it was known then) was trying to grow beyond its dot-com roots, into the enterprise space and guys like EMC and HDS were frequently trying to put NetApp down.

It’s a toy”  was the most common jibe I got in regular engagements until EMC suddenly decided to attack Network Appliance directly with their EMC CLARiiON IP4700. EMC guys would fondly remember this as the “NetApp killer“. Continue reading

Why demote archived data access?

We are all familiar with the concept of data archiving. Passive data gets archived from production storage and are migrated to a slower and often, cheaper storage medium such tapes or SATA disks. Hence the terms nearline and offline data are created. With that, IT constantly reminds users that the archived data is infrequently accessed, and therefore, they have to accept the slower access to passive, archived data.

The business conditions have certainly changed, because the need for data to be 100% online is becoming more relevant. The new competitive nature of businesses dictates that data must be at the fingertips, because speed and agility are the new competitive advantage. Often the total amount of data, production and archived data, is into hundred of TBs, even into PetaBytes!

The industries I am familiar with – Oil & Gas, and Media & Entertainment – are facing this situation. These industries have a deluge of files, and unstructured data in its archive, and much of it dormant, inactive and sitting on old tapes of a bygone era. Yet, these files and unstructured data have the most potential to be explored, mined and analyzed to realize its value to the organization. In short, the archived data and files must be democratized!

The flip side is, when the archived files and unstructured data are coupled with a slow access interface or unreliable storage infrastructure, the value of archived data is downgraded because of the aggravated interaction between access and applications and business requirements. How would organizations value archived data more if the access path to the archived data is so damn hard???!!!

An interesting solution fell upon my lap some months ago, and putting A and B together (A + B), I believe the access path to archived data can be unbelievably of high performance, simple, transparent and most importantly, remove the BLOODY PAIN of FILE AND DATA MIGRATION!  For storage administrators and engineers familiar with data migration, especially if the size of the migration is into hundreds of TBs or even PBs, you know what I mean!

I have known this solution for some time now, because I have been avidly following its development after its founders left NetApp following their Spinnaker venture to start Avere Systems.

avere_220

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Praying to the hypervisor God

I was reading a great article by Frank Denneman about storage intelligence moving up the stack. It was pretty much in line with what I have been observing in the past 18 months or so, about the storage pendulum having swung back to DAS (direct attached storage). To be more precise, the DAS form factor I am referring to are physical server hardware that houses many disk drives.

Like it or not, the hypervisor has become the center of the universe in the IT space. VMware has become the indomitable force in the hypervisor technology, with Microsoft Hyper-V playing catch-up. The seismic shift of these 2 hypervisor technologies are leading storage vendors to place them on to the altar and revering them as deities. The others, with the likes of Xen and KVM, and to lesser extent Solaris Containers aren’t really worth mentioning.

This shift, as the pendulum swings from networked storage back to internal “direct-attached” storage are dictated by 4 main technology factors:

  • The x86 server architecture
  • Software-defined
  • Scale-out architecture
  • Flash-based storage technology

Anyone remember Thumper? Not the Disney character from the Bambi movie!

thumper-bambi-cartoon-character

When the SunFire X4500 (aka Thumper) was first released in (intermission: checking Wiki for the right year) in 2006, I felt that significant wound inflicted in the networked storage industry. Instead of the usual 4-8 hard disk drives in the all the industry servers at the time, the X4500 4U chassis housed 48 hard disk drives. The design and architecture were so astounding to me, I even went and bought a 1U SunFire X4150 for my personal server collection. Such was my adoration for Sun’s technology at the time.

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Technology prowess of Riverbed SteelFusion

The Riverbed SteelFusion (aka Granite) impressed me the moment it was introduced to me 2 years ago. I remembered that genius light bulb moment well, in December 2012 to be exact, and it had left its mark on me. Like I said last week in my previous blog, the SteelFusion technology is unique in the industry so far and has differentiated itself from its WAN optimization competitors.

To further understand the ability of Riverbed SteelFusion, a deeper inspection of the technology is essential. I am fortunate to be given the opportunity to learn more about SteelFusion’s technology and here I am, sharing what I have learned.

What does the technology of SteelFusion do?

Riverbed SteelFusion takes SAN volumes from supported storage vendors in the central datacenter and projects the storage volumes (aka LUNs)to applications and hosts at the remote branches. The technology requires a paired relationship between SteelFusion Core (in the centralized datacenter) and SteelFusion Edge (at the branch). Both SteelFusion Core and Edge are fronted respectively by the Riverbed SteelHead WAN optimization device, to deliver the performance required.

The diagram below gives an overview of how the entire SteelFusion network architecture is like:

Riverbed SteelFusion Overall Solution 2 Continue reading