Emerging and disruptive markets are hard to quantify and forecast: They often apply different marketing labels for the same thing, and have no baseline industry data and no consistent methods of measurement and forecasting.
But this recent Wibikon big data report is head and hands above others. This is the third edition of the report and I wanted to give a shout-out to the authors – Jeff Kelly, David Vellante and David Foyer – on this best-in-class body of work.
Behind the numbers: The way I see it, big data has two different markets with very different technology and investment requirements and pace of adoption:
And now, the color commentary on the Wikibon big data report …
One of the coolest parts of my job is talking with customers and partners about their production environment challenges around database technology. A topic of particular interest lately is in-memory database (IMDB) systems and their integration into an existing environment.
The need for speed
Much of the media coverage of IMDB integrations is heavily focused on speed and loaded with terms like real-time processing, on-demand analytics and memory speed. But zeroing in on the performance benefits comes at the expense of so many other key aspects of IMDBs.
I was asked some interesting questions recently by CEO & CIO, a Chinese business magazine. The questions ranged from how Chinese Internet giants like Alibaba, Baidu and Tencent differ from other customers and what leading technologies big Internet companies have created to questions about emerging technologies such as software-defined storage (SDS) and software-defined datacenters (SDDC) and changes in the ecosystem of datacenter hardware, software and service providers. These were great questions. Sometimes you need the press or someone outside the industry to ask a question that makes you step back and think about what’s going on.
My first blog in this series, “How to maximize performance of PCIe flash for enterprise applications running on Linux,” describes the steps for aligning PCIe® flash devices. This blog covers the next stage of setting up the PCIe flash device when using the Linux® operating system: creating a RAW device or a file system.
At this point, one or more PCIe flash cards have been partitioned on a sector boundary. Depending on their use, these partitioned devices are either set up as a single RAW device or as part of a logical volume or RAID array.
I was recently speaking to a customer about data reduction technology and I remembered a conversation I had with my mother when I was a teenager. She used to complain how chaotic my bedroom looked, and one time I told her “I was illustrating the second law of thermodynamics” for my physics class. I was referring to the mess and the tendency of things to evolve towards the state of maximum entropy, or randomness. I have to admit I only used that line once with my mom because it pissed her off and she likened me to an intelligent donkey.
Customer dilemma: I just purchased PCIe® flash cards to increase performance of my enterprise applications that run on Linux® and Unix®. How do I set them up to get the best performance?
Good question. I wish there were a simple answer but each environment is different. There is no cookie-cutter configuration that fits all, though a few questions will reveal how the PCIe flash cards should be configured for optimum performance.
Most of the popular relational and non-relational databases run on many different operating systems.
Turn on your smart phone and it works like charm. But explosive global adoption of smart phones with feature-rich applications is stressing mobile networks like never before. For mobile network providers, the challenge couldn’t be more acute: Find new ways to deliver more mobile bandwidth even as the average revenue per user remains flat.
In this AIS interview, LSI’s Jeff Connell, director of mobile networking product marketing, talks about how network providers are turning to heterogenous networks (HetNets) to reduce the cost of deploying, scaling and managing mobile networks.
Hadoop has grown from an identity-challenged adolescent, a budding technology unsure of which use cases to call its own, to a fairly mature young adult with its most recent release of Hadoop® 2.0. Apache™ Hadoop® was introduced in 2007 with the primary intent to provide MapReduce-based batch processing for big data. While the original Hadoop certainly has made a big impact on how we use big data, it also had its limitations, chief among them:
It’s the start of the new year, and it’s traditional to make predictions – right? But predicting the future of the datacenter has been hard lately. There have been and continue to be so many changes in flight that possibilities spin off in different directions. Fractured visions through a kaleidoscope. Changes are happening in the businesses behind datacenters, the scale, the tasks and what is possible to accomplish, the value being monetized, and the architectures and technologies to enable all of these.
A major reason enterprise customers see high latency and poorer than expected performance when implementing flash technology is that the flash partition is not aligned on a sector boundary that allows the flash device to access its data efficiently. When creating a Logical Volume (LVM), things can even get more complicated. Proper partition alignment is critical to performance when implementing flash in your enterprise.
An aligned partition is one that starts on a sector number that’s evenly divisible by 4k, or 8k, or a starting sector that is divisible by eight.