Rethinking data processing frameworks systems in real time

“Row, row, row your boat, gently down the stream…”

Except the stream isn’t gentle at all in the data processing’s new context.

For many of us in the storage infrastructure and data management world, the well known framework is storing and retrieve data from a storage media. That media could be a disk-based storage array, a tape, or some cloud storage where the storage media is abstracted from the users and the applications. The model of post processing the data after the data has safely and persistently stored on that media is a well understood and a mature one. Users, applications and workloads (A&W) process this data in its resting phase, retrieve it, work on it, and write it back to the resting phase again.

There is another model of data processing that has been bubbling over the years and now reaching a boiling point. Still it has not reached its apex yet. This is processing the data in flight, while it is still flowing as it passes through processing engine. The nature of this kind of data is described in one 2018 conference I chanced upon a year ago.

letgo marketplace processing numbers in 2018

  • * NRT = near real time

From a storage technology infrastructure perspective, this kind of data processing piqued my curiosity immensely. And I have been studying this burgeoning new data processing model in my spare time, and where it fits, bringing the understanding back into the storage infrastructure and data management side.

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What the heck is Storage Modernization?

We often hear the word “modernization” thrown around these days. The push is to get the end user to refresh their infrastructure, and the storage infrastructure market is rife with modernization word. Is your storage ripe for “modernization“?

Many possibilities to modernize storage

To modernize, it has to be relative to legacy storage hardware, and the operating environment that came with it. But if the so-called “legacy” still does the job, should you modernize?

Big Data is right

When the word “Big Data” came into prominence a while back, it stirred the IT industry into a frenzy. At one point, Apache Hadoop became the poster elephant (pun intended) for this exciting new segment. So many Vs came out, but I settled with 4 Vs as the framework of my IT conversations. The 4Vs we often hear are:

  • Volume
  • Velocity
  • Variety
  • Veracity

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Hadoop is truly dead – LOTR version

[Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies to be presented at this event. The content of this blog is of my own opinions and views]

This blog was not intended because it was not in my plans to write it. But a string of events happened in the Storage Field Day 19 week and I have the fodder to share my thoughts. Hadoop is indeed dead.

Warning: There are Lord of the Rings references in this blog. You might want to do some research. 😉

Storage metrics never happened

The fellowship of Arjan Timmerman, Keiran Shelden, Brian Gold (Pure Storage) and myself started at the office of Pure Storage in downtown Mountain View, much like Frodo Baggins, Samwise Gamgee, Peregrine Took and Meriadoc Brandybuck forging their journey vows at Rivendell. The podcast was supposed to be on the topic of storage metrics but was unanimously swung to talk about Hadoop under the stewardship of Mr. Stephen Foskett, our host of Tech Field Day. I saw Stephen as Elrond Half-elven, the Lord of Rivendell, moderating the podcast as he would have in the plans of decimating the One Ring in Mount Doom.

So there we were talking about Hadoop, or maybe Sauron, or both.

The photo of the Oliphaunt below seemed apt to describe the industry attacks on Hadoop.

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Commvault big bet

I woke up at 2.59am in the morning of Sept 5th morning, a bit discombobulated and quickly jumped into the Commvault call. The damn alarm rang and I slept through it, but I got up just in time for the 3am call.

As I was going through the motion of getting onto UberConference, organized by GestaltIT, I was already sensing something big. In the call, Commvault was acquiring Hedvig and it hit me. My drowsy self centered to the big news. And I saw a few guys from Veritas and Cohesity on my social media group making gestures about the acquisition.

I spent the rest of the week thinking about the acquisition. What is good? What is bad? How is Commvault going to move forward? This is at pressing against the stark background from the rumour mill here in South Asia, just a week before this acquisition news, where I heard that the entire Commvault teams in Malaysia and Asia Pacific were released. I couldn’t confirm the news in Asia Pacific, but the source of the news coming from Malaysia was strong and a reliable one.

What is good?

It is a big win for Hedvig. Nestled among several scale-out primary storage vendors and little competitive differentiation, this Commvault acquisition is Hedvig’s pay day.

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Oracle Cloud Infrastructure to prove skeptics wrong

[Preamble: I have been invited by  GestaltIT as a delegate to their TechFieldDay from Oct 17-19, 2018 in the Silicon Valley USA. My expenses, travel and accommodation are covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

The much maligned Oracle Cloud is getting a fresh reboot, starting with their Oracle Cloud Infrastructure (OCI), and significant enhancements and technology updates were announced at the Oracle Open World this week. I had the privilege to hear about Oracle Cloud’s new attack plan when they presented at Tech Field Day 17 last week.

Oracle Cloud has not have the best of days in recent months. Thomas Kurian’s resignation as their President of Product Development was highly publicized in a disagreement with CTO and founder, Larry Ellison over cloud software strategy. Then there was an on-going lawsuit about how Oracle was misrepresenting their cloud revenue growth, which puts Oracle in a bad light.

On the local front here in Malaysia, I have heard from the grapevine of the aggressive nature of Oracle personnel pushing partners and customers to adopt their cloud services using legal scare tactics on their database licensing. A buddy of mine, who was previously the cloud business development manager at CTC Global, also shared Oracle’s cloud shortcomings compared to Amazon Web Service and Microsoft Azure a year ago.

Oracle Cloud Infrastructure team aimed to turnover the bad perceptions, starting with the delegates of Tech Field Day 17, including yours truly.Their strategy was clear. Oracle Cloud Infrastructure runs the highest performance and the highest enterprise grade Infrastructure-as-a-Service (IaaS), bar none. Unlike the IBM Cloud, which in my opinion is a wishy-washy cloud service platform, Oracle Cloud’s ambition is solid.

They did a demo on JDEdwards EnterpriseOne application, and they continue to demonstrate their prowess running the highest performance computing experience ever, for all enterprise-grade workload. And that enterprise pedigree is clear.

Just this week, Amazon Prime Day had an outage. Amazon is in the process of weaning Oracle database from their entire ecosystem by 2020, and this outage clearly showed that the Oracle database and the enterprise applications would only run best on Oracle Cloud Infrastructure.

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The Network is Still the Computer

[Preamble: I have been invited by  GestaltIT as a delegate to their TechFieldDay from Oct 17-19, 2018 in the Silicon Valley USA. My expenses, travel and accommodation are covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Sun Microsystems coined the phrase “The Network is the Computer“. It became one of the most powerful ideologies in the computing world, but over the years, many technology companies have tried to emulate and practise the mantra, but fell short.

I have never heard of Drivescale. It wasn’t in my radar until the legendary NFS guru, Brian Pawlowski joined them in April this year. Beepy, as he is known, was CTO of NetApp and later at Pure Storage, and held many technology leadership roles, including leading the development of NFSv3 and v4.

Prior to Tech Field Day 17, I was given some “homework”. Stephen Foskett, Chief Cat Herder (as he is known) of Tech Field Days and Storage Field Days, highly recommended Drivescale and asked the delegates to pick up some notes on their technology. Going through a couple of the videos, Drivescale’s message and philosophy resonated well with me. Perhaps it was their Sun Microsystems DNA? Many of the Drivescale team members were from Sun, and I was previously from Sun as well. I was drinking Sun’s Kool Aid by the bucket loads even before I graduated in 1991, and so what Drivescale preached made a lot of sense to me.Drivescale is all about Scale-Out Architecture at the webscale level, to address the massive scale of data processing. To understand deeper, we must think about “Data Locality” and “Data Mobility“. I frequently use these 2 “points of discussion” in my consulting practice in architecting and designing data center infrastructure. The gist of data locality is simple – the closer the data is to the processing, the cheaper/lightweight/efficient it gets. Moving data – the data mobility part – is expensive.

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Cohesity SpanFS – a foundational shift

[Preamble: I was a delegate of Storage Field Day 15 from Mar 7-9, 2018. My expenses, travel and accommodation were paid for by GestaltIT, the organizer and I was not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

Cohesity SpanFS impressed me. Their filesystem was designed from ground up to meet the demands of the voluminous cloud-scale data, and yes, the sheer magnitude of data everywhere needs to be managed.

We all know that primary data is always the more important piece of data landscape but there is a growing need to address the secondary data segment as well.

Like a floating iceberg, the piece that is sticking out is the more important primary data but the larger piece beneath the surface of the water, which is the secondary data, is becoming more valuable. Applications such as file shares, archiving, backup, test and development, and analytics and insights are maturing as the foundational data management frameworks and fast becoming the bedrock of businesses.

The ability of businesses to bounce back after a disaster; the relentless testing of large data sets to develop new competitive advantage for businesses; the affirmations and the insights of analyzing data to reduce risks in decision making; all these are the powerful back engine applicability that thrust businesses forward. Even the ability to search for the right information in a sea of data for regulatory and compliance reasons is part of the organization’s data management application.

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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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