Rick Kutcipal, board member of the SCSI Trade Association and a product marketing manager at Avago, authored a piece on the role of SAS in the data center for Network Computing.
An excerpt from the article:
SAS is a high-performance, full-duplex storage protocol that today operates at speeds of up to 12 Gb/s and is backward compatible to 6 Gb/s and 3 Gb/s speeds. These data rates, coupled with the fact that SAS is less expensive than competing technologies, makes SAS a cost-effective option for a broad range of data center storage applications.
When looking at the near future of data center technology, there are two very important trends to consider. First—the adoption of public and private cloud computing continues to become much more pervasive. Enterprises, software developers, and home users are all making the transition to cloud-based models for services and storage.
Second—devices, data, and network demand are all projected to grow at explosive rates over the next few years. By 2020, the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes1.
Ever since SandForce introduced data reduction technology with the DuraWrite™ feature in 2009, some users have been confused about how it works and questioned whether it delivers the benefits we claim. Some even believe there are downsides to using DuraWrite with an SSD. In this blog, I will dispel those misconceptions.
Data reduction technology refresher
Four of my previous blogs cover the many advantages of using data reduction technology like DuraWrite:
A couple of years ago I got a DSLR (digital single-lens reflex) camera. After using a compact digital camera, the DSLR opened a new world of photography for me. It was great to have the option to shoot six frames per second, use different lenses and fine-tune shutter speed, exposure and other parameters.
Learning to take my photography to a higher level, from auto to manual settings, was quite an experience. Through research and talking to friends and photographers, I discovered that I needed to learn these fundamentals:
Experimenting with each of these variables was a frustrating test of Murphy’s Law.
I started working years ago to engage large datacenters, learn what their problems are and try to craft solutions for their problems. It’s taken years, but we engaged them, learned, changed how we thought about storage and began creating solutions that are being deployed at scale.
We’ve started to do the same with the Chinese Internet giants. They’re growing at an incredible rate. They have similar problems, but it’s surprising how different their solution approaches are. Each one is unique. And we’re constantly learning from these guys.
Big data, it’s the buzz word of the year and it’s generating a lot of attention. An incalculable number of articles fervently repeat the words “variety, velocity and volume,” citing click streams, RFID tags, email, surveillance cameras, Twitter® feeds, Facebook® posts, Flickr® images, blog musings, YouTube® videos, cellular texting, healthcare monitoring …. (gasps for air). We have become a society that sweats buckets of data every day (the latest estimates are approximately 34GB per person every 24 hours) and businesses are scrambling to capture all this information to learn more about us.
Software-defined datacenters (SDDC) and software-defined storage (SDS) are big movements in the industry right now. Just read the trade press or attend any conference and you’ll see that – it’s a big deal. We’re seeing for-pay vendors providing solutions, as well as strong ecosystems evolving around open source solutions. It’s not surprising why – there is a need for enterprises to deploy large scale compute clusters, and that takes either deep expertise that’s very rare, or orchestration tools that have not existed in the past.
During the past few years, the deployment of cloud architectures has accelerated to support various consumer and enterprise applications such as email, word processing, enterprise resource planning, customer relationship management and the like. Traditionally, co-located servers, storage and networking moved to the cloud en masse in the form of a service, with overlying applications that have been and remain very insensitive to delay and jitter.
But the fast-emerging next generation of business applications require much tighter service level agreements (SLA) from cloud providers.
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.