Welcome!

Microservices Expo Authors: Stackify Blog, Aruna Ravichandran, Dalibor Siroky, Kevin Jackson, PagerDuty Blog

Related Topics: @DXWorldExpo, Java IoT, Microservices Expo, Containers Expo Blog, @CloudExpo, SDN Journal

@DXWorldExpo: Article

Archiving the Big Data Old Tail

At any point in time, half of your Big Data are more than two years old

Scenario #1: out of the blue, your boss calls, looking for some long-forgotten entry in a spreadsheet from 1989. Where do you look? Or consider scenario #2: said boss calls again, only this time she wants you to analyze customer purchasing behavior...going back to 1980. Similar problem, only instead of finding a single datum, you must find years of ancient information and prepare it for analysis with a modern business intelligence tool.

The answer, of course, is archiving. Fortunately, you (or your predecessor, or predecessor's predecessor) have been archiving important-or potentially important-corporate data since your organization first started using computers back in the 1960s. So all you have to do to keep your boss happy is find the appropriate archives, recover the necessary data, and you're good to go, right?

Not so fast. There are a number of gotchas to this story, some more obvious than others. Cloud to the rescue? Perhaps, but many archiving challenges remain, and the Cloud actually introduces some new speed bumps as well. Now factor in Big Data. Sure, Big Data are big, so archiving Big Data requires a big archive. Lucky you-vendors have already been knocking on your door peddling Big Data archiving solutions. Now can you finally breathe easy? Maybe, maybe not. Here's why.

Archiving: The Long View
So much of our digital lives have taken place over the last twenty years or so that we forget that digital computing dates back to the 1940s-and furthermore, we forget that this sixty-odd year lifetime of the Information Age is really only the first act of perhaps centuries of computing before humankind either evolves past zeroes and ones altogether or kills itself off in the process. Our technologies for archiving information, however, are woefully shortsighted, for several reasons:

  • Hardware obsolescence (three to five years) - Using a hard drive or tape drive for archiving? It won't be long till the hardware is obsolete. You may get more life out of the gear you own, but one it wears out, you'll be stuck. Anyone who archived to laser disk in the 1980s has been down this road.
  • File format obsolescence (five to ten years) - True, today's Office products can probably read that file originally saved in the Microsoft Excel version 1 file format back in the day, but what about those VisiCalc or Lotus 123 files? Tools that will convert such files to their modern equivalents will eventually grow increasingly scarce, and you always risk the possibility that they won't handle the conversion properly, leading to data corruption. If your data are encrypted, then your encryption format falls into the file format obsolescence bucket as well. And what about the programs themselves? From simple spreadsheet formulas to complex legacy spaghetti code, how do you archive algorithms in an obsolescence-proof format?
  • Media obsolescence (ten to fifteen years) - CD-ROMs and digital backup tapes have an expected lifetime. Keeping them cool and dry can extend their life, but actually using them will shorten it. Do you really want to rely upon a fifteen-year-old backup tape for critical information?
  • Computing paradigm obsolescence (fifty years perhaps; it's anybody's guess) - will quantum computing or biological processors or some other futuristic gear drive binary digital technologies into the Stone Age? Only time will tell. But if you are forward thinking enough to archive information for the 22nd century, there's no telling what you'll need to do to maintain the viability of your archives in a post-binary world.

Cloud to the Rescue?
On the surface, letting your Cloud Service Provider (CSP) archive your data solves many of these issues. Not only are the new archiving services like Amazon Glacier impressively cost-effective, but we can feel reasonably comfortable counting on today's CSPs to migrate our data from one hardware/media platform to the next over time as technology advances. So, can Cloud solve all your archiving issues?

At some point the answer may be yes, but Cloud Computing is still far too immature to jump to such a conclusion. Will your CSP still be in business decades from now? As the CSP market undergoes its inevitable consolidation phase, will the new CSP who bought out your old CSP handle your archive properly? Only time will tell.

But even if the CSPs rise to the archiving challenge, you may still have the file format challenge. Sure, archiving those old Lotus 123 files in the Cloud is a piece of cake, but that doesn't mean that your CSP will return them in Excel version 21.3 format ten years hence-an unfortunate and unintentional example of garbage in the Cloud.

The Big Data Old Tail
You might think that the challenges inherent in archiving Big Data are simply a matter of degree: bigger storage for bigger data sets, right? But thinking of Big Data as little more than extra-large data sets misses the big picture of the importance of Big Data.

The point to Big Data is that the indicated data sets continue to grow in size on an ongoing basis, continually pushing the limits of existing technology. The more capacity available for storage and processing, the larger the data sets we end up with. In other words, Big Data are by definition a moving target.

One familiar estimate states that the quantity of data in the world doubles every two years. Your organization's Big Data may grow somewhat faster or slower than this convenient benchmark, but in any case, the point is that Big Data growth is exponential. So, taking the two-year doubling factor as a rule of thumb, we can safely say that at any point in time, half of your Big Data are less than two years old, while the other half of your Big Data are more than two years old. And of course, this ZapFlash is concerned with the older half.

The Big Data archiving challenge, therefore, is breaking down the more-than-two-years-old Big Data sets. Remember that this two-year window is true at any point in time. Thinking about the problem mathematically, then, you can conclude that a quarter of your Big Data are more than four years old, an eighth are more than six years old, etc.

Combine this math with the lesson of the first part of this ZapFlash, and a critical point emerges: byte for byte, the cost of maintaining usable archives increases the older those archives become. And yet, the relative size of those archives is vanishingly small relative to today's and tomorrow's Big Data. Furthermore, this problem will only get worse over time, because the size of the Old Tail continues to grow exponentially.

We call this Big Data archiving problem the Big Data Old Tail. Similar to the Long Tail argument, which focuses on the value inherent in summing up the Long Tail of customer demand for niche products, the Big Data Old Tail focuses on the costs inherent in maintaining archives of increasingly small, yet increasingly costly data as we struggle to deal with older and older information. True, perhaps the fact that the Old Tail data sets from a particular time period are small will compensate for the fact that they are costly to archive, but remember that the Old Tail continues to grow over time. Unless we deal with the Old Tail, it threatens to overwhelm us.

The ZapThink Take
The obvious question that comes to mind is whether we need to save all those old data sets anyway. After all, who cares about, say, purchasing data from 1982? And of course, you may have a business reason for deleting old information. Since information you preserve may be subject to lawsuits or other unpleasantness, you may wish to delete data once it's legal to do so.

Fair enough. But there are perhaps far more examples of Big Data sets that your organization will wish to preserve indefinitely than data sets you're happy to delete. From scientific data to information on market behavior to social trends, the richness of our Big Data do not simply depend on the information from the last year or two or even ten. After all, if we forget the mistakes of the past then we are doomed to repeat them. Crunching today's Big Data can give us business intelligence, but only by crunching yesterday's Big Data as well can we ever expect to glean wisdom from our information.

More Stories By Jason Bloomberg

Jason Bloomberg is the leading expert on architecting agility for the enterprise. As president of Intellyx, Mr. Bloomberg brings his years of thought leadership in the areas of Cloud Computing, Enterprise Architecture, and Service-Oriented Architecture to a global clientele of business executives, architects, software vendors, and Cloud service providers looking to achieve technology-enabled business agility across their organizations and for their customers. His latest book, The Agile Architecture Revolution (John Wiley & Sons, 2013), sets the stage for Mr. Bloomberg’s groundbreaking Agile Architecture vision.

Mr. Bloomberg is perhaps best known for his twelve years at ZapThink, where he created and delivered the Licensed ZapThink Architect (LZA) SOA course and associated credential, certifying over 1,700 professionals worldwide. He is one of the original Managing Partners of ZapThink LLC, the leading SOA advisory and analysis firm, which was acquired by Dovel Technologies in 2011. He now runs the successor to the LZA program, the Bloomberg Agile Architecture Course, around the world.

Mr. Bloomberg is a frequent conference speaker and prolific writer. He has published over 500 articles, spoken at over 300 conferences, Webinars, and other events, and has been quoted in the press over 1,400 times as the leading expert on agile approaches to architecture in the enterprise.

Mr. Bloomberg’s previous book, Service Orient or Be Doomed! How Service Orientation Will Change Your Business (John Wiley & Sons, 2006, coauthored with Ron Schmelzer), is recognized as the leading business book on Service Orientation. He also co-authored the books XML and Web Services Unleashed (SAMS Publishing, 2002), and Web Page Scripting Techniques (Hayden Books, 1996).

Prior to ZapThink, Mr. Bloomberg built a diverse background in eBusiness technology management and industry analysis, including serving as a senior analyst in IDC’s eBusiness Advisory group, as well as holding eBusiness management positions at USWeb/CKS (later marchFIRST) and WaveBend Solutions (now Hitachi Consulting).

@MicroservicesExpo Stories
How is DevOps going within your organization? If you need some help measuring just how well it is going, we have prepared a list of some key DevOps metrics to track. These metrics can help you understand how your team is doing over time. The word DevOps means different things to different people. Some say it a culture and every vendor in the industry claims that their tools help with DevOps. Depending on how you define DevOps, some of these metrics may matter more or less to you and your team.
For many of us laboring in the fields of digital transformation, 2017 was a year of high-intensity work and high-reward achievement. So we’re looking forward to a little breather over the end-of-year holiday season. But we’re going to have to get right back on the Continuous Delivery bullet train in 2018. Markets move too fast and customer expectations elevate too precipitously for businesses to rest on their laurels. Here’s a DevOps “to-do list” for 2018 that should be priorities for anyone w...
If testing environments are constantly unavailable and affected by outages, release timelines will be affected. You can use three metrics to measure stability events for specific environments and plan around events that will affect your critical path to release.
In a recent post, titled “10 Surprising Facts About Cloud Computing and What It Really Is”, Zac Johnson highlighted some interesting facts about cloud computing in the SMB marketplace: Cloud Computing is up to 40 times more cost-effective for an SMB, compared to running its own IT system. 94% of SMBs have experienced security benefits in the cloud that they didn’t have with their on-premises service
DevOps failure is a touchy subject with some, because DevOps is typically perceived as a way to avoid failure. As a result, when you fail in a DevOps practice, the situation can seem almost hopeless. However, just as a fail-fast business approach, or the “fail and adjust sooner” methodology of Agile often proves, DevOps failures are actually a step in the right direction. They’re the first step toward learning from failures and turning your DevOps practice into one that will lead you toward even...
DevOps is under attack because developers don’t want to mess with infrastructure. They will happily own their code into production, but want to use platforms instead of raw automation. That’s changing the landscape that we understand as DevOps with both architecture concepts (CloudNative) and process redefinition (SRE). Rob Hirschfeld’s recent work in Kubernetes operations has led to the conclusion that containers and related platforms have changed the way we should be thinking about DevOps and...
While walking around the office I happened upon a relatively new employee dragging emails from his inbox into folders. I asked why and was told, “I’m just answering emails and getting stuff off my desk.” An empty inbox may be emotionally satisfying to look at, but in practice, you should never do it. Here’s why. I recently wrote a piece arguing that from a mathematical perspective, Messy Desks Are Perfectly Optimized. While it validated the genius of my friends with messy desks, it also gener...
The goal of Microservices is to improve software delivery speed and increase system safety as scale increases. Microservices being modular these are faster to change and enables an evolutionary architecture where systems can change, as the business needs change. Microservices can scale elastically and by being service oriented can enable APIs natively. Microservices also reduce implementation and release cycle time and enables continuous delivery. This paper provides a logical overview of the Mi...
The next XaaS is CICDaaS. Why? Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But… securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous ar...
The enterprise data storage marketplace is poised to become a battlefield. No longer the quiet backwater of cloud computing services, the focus of this global transition is now going from compute to storage. An overview of recent storage market history is needed to understand why this transition is important. Before 2007 and the birth of the cloud computing market we are witnessing today, the on-premise model hosted in large local data centers dominated enterprise storage. Key marketplace play...
The cloud revolution in enterprises has very clearly crossed the phase of proof-of-concepts into a truly mainstream adoption. One of most popular enterprise-wide initiatives currently going on are “cloud migration” programs of some kind or another. Finding business value for these programs is not hard to fathom – they include hyperelasticity in infrastructure consumption, subscription based models, and agility derived from rapid speed of deployment of applications. These factors will continue to...
Some people are directors, managers, and administrators. Others are disrupters. Eddie Webb (@edwardawebb) is an IT Disrupter for Software Development Platforms at Liberty Mutual and was a presenter at the 2016 All Day DevOps conference. His talk, Organically DevOps: Building Quality and Security into the Software Supply Chain at Liberty Mutual, looked at Liberty Mutual's transformation to Continuous Integration, Continuous Delivery, and DevOps. For a large, heavily regulated industry, this task ...
Following a tradition dating back to 2002 at ZapThink and continuing at Intellyx since 2014, it’s time for Intellyx’s annual predictions for the coming year. If you’re a long-time fan, you know we have a twist to the typical annual prediction post: we actually critique our predictions from the previous year. To make things even more interesting, Charlie and I switch off, judging the other’s predictions. And now that he’s been with Intellyx for more than a year, this Cortex represents my first ...
"Grape Up leverages Cloud Native technologies and helps companies build software using microservices, and work the DevOps agile way. We've been doing digital innovation for the last 12 years," explained Daniel Heckman, of Grape Up in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
The Toyota Production System, a world-renowned production system is based on the "complete elimination of all waste". The "Toyota Way", grounded on continuous improvement dates to the 1860s. The methodology is widely proven to be successful yet there are still industries within and tangential to manufacturing struggling to adopt its core principles: Jidoka: a process should stop when an issue is identified prevents releasing defective products
We seem to run this cycle with every new technology that comes along. A good idea with practical applications is born, then both marketers and over-excited users start to declare it is the solution for all or our problems. Compliments of Gartner, we know it generally as “The Hype Cycle”, but each iteration is a little different. 2018’s flavor will be serverless computing, and by 2018, I mean starting now, but going most of next year, you’ll be sick of it. We are already seeing people write such...
Defining the term ‘monitoring’ is a difficult task considering the performance space has evolved significantly over the years. Lately, there has been a shift in the monitoring world, sparking a healthy debate regarding the definition and purpose of monitoring, through which a new term has emerged: observability. Some of that debate can be found in blogs by Charity Majors and Cindy Sridharan.
It’s “time to move on from DevOps and continuous delivery.” This was the provocative title of a recent article in ZDNet, in which Kelsey Hightower, staff developer advocate at Google Cloud Platform, suggested that “software shops should have put these concepts into action years ago.” Reading articles like this or listening to talks at most DevOps conferences might make you think that we’re entering a post-DevOps world. But vast numbers of organizations still struggle to start and drive transfo...
Let's do a visualization exercise. Imagine it's December 31, 2018, and you're ringing in the New Year with your friends and family. You think back on everything that you accomplished in the last year: your company's revenue is through the roof thanks to the success of your product, and you were promoted to Lead Developer. 2019 is poised to be an even bigger year for your company because you have the tools and insight to scale as quickly as demand requires. You're a happy human, and it's not just...
"Opsani helps the enterprise adopt containers, help them move their infrastructure into this modern world of DevOps, accelerate the delivery of new features into production, and really get them going on the container path," explained Ross Schibler, CEO of Opsani, and Peter Nickolov, CTO of Opsani, in this SYS-CON.tv interview at DevOps Summit at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.