Posts Tagged ‘splunk’

Dell EMC extends its portfolio for Splunk to VxRack FLEX

Brett Roberts

Brett Roberts

Data Analytics Systems Engineer at Dell EMC
Brett is the Technical Lead for Dell EMC’s Data Analytics Technology Alliances, focused on developing solutions that help customers solve their data challenges. You can find him on social media at @Broberts2261

Operational Intelligence and machine generated data have been very hot topics lately as organizations are beginning to realize how valuable this data is for the business. For the last few years, Splunk has been the leader in this space with their all-encompassing platform that enables the ability to collect, search and analyze machine generated data. (Not up to speed on this yet? Check out my other blog on getting started with machine generated data) Dell EMC and Splunk have had a tremendous partnership over the past couple years that is based on the premise that we offer market leading infrastructure that is optimal for Splunk’s world class analytics platform for machine generated data. A couple weeks ago, we took this one step further… I’m excited to announce the release of the Solution Guide for Machine Analytics with Splunk Enterprise on VxRack Flex 1000! With this, Dell EMC now has a validated rack scale, hyper-converged infrastructure solution for Splunk that has been jointly validated by Splunk & Dell EMC.

Why is this important?

Having this solution that has been jointly validated by both Splunk and Dell EMC to “meet or exceed Splunk’s performance benchmarks” gives users a higher degree of confidence in the environment. With this solution the performance needed to run Splunk effectively and gain the valuable insights to make critical IT and business decisions will be there. Our solutions engineering team along with Splunk put hundreds of engineering hours into designing specific configurations based on a variety of different deployment scenarios and rigorously tested them to ensure performance. The solutions guide gives you not only those configurations but also implementation guidelines and deployment practices. All of this equals lower risk, quicker time to value and validated for performance…can’t ask for anything better.

How is VxRack Optimal for Splunk?

VxRack provides flexible, rack scale, hyper-converged infrastructure that allows you to use the hypervisor of your choice or bare metal as well as the ability to start small but scale-out to thousands of nodes. With VxRack you are given the flexibility to optimize your tiering for Splunk by putting Hot and Warm buckets in SSD while using HHD or even Isilon scale-out NAS for your cold bucket needs (Solution guide shows how to use Isilon for cold tiering). You also get to enjoy the benefits of Software Defined Storage and data services that are essential in today’s data center. The best part is that VxRack gives a turnkey experience that is engineered and designed to be ready to run, giving you a quicker time to insight and value. Additionally, with single support and life-cycle management for your infrastructure you lower complexity and reduce risk and costs. All of this equals great performance, economical tiering structure & easy to deploy and manage infrastructure that is validated to run Splunk.

Getting started with machine-generated data

Brett Roberts

Brett Roberts

Data Analytics Systems Engineer at Dell EMC
Brett is the Technical Lead for Dell EMC’s Data Analytics Technology Alliances, focused on developing solutions that help customers solve their data challenges. You can find him on social media at @Broberts2261

By Brett Roberts with Debra Slapak

We are literally surrounded by data generated from devices and other machines—things like the phones in our pockets, vehicle sensors, the ATM at our favorite spot, cameras on the street, even the thermostats and appliances in our homes. As consumers, we benefit from insights generated when this data is analyzed and put to work for us. This, ideally, protects us or makes us more loyal to the companies that provide better experiences or outcomes for us.

Increasingly, business, government and non-profit organizations alike are generating, capturing, and analyzing massive amounts of machine-generated data to help them (more…)

Big Data Conversation with Splunk

Erin K. Banks

Erin K. Banks

Portfolio Marketing Director at Dell EMC
Erin K. Banks has been in the IT industry for almost 20 years. She is the Portfolio Marketing Director for Big Data and Data Analytics at Dell EMC. Previously she worked at Juniper Networks in Technical Marketing for the Security Business Unit. She has also worked at VMware and EMC as an SE in the Federal Division, focused on Virtualization and Security. She holds both CISSP and CISA accreditations. Erin has a BS in Electrical Engineering and is an author, blogger, and avid runner. You can find her on social media at @banksek
Erin K. Banks

I had the opportunity to talk with Jon Rooney, Senior Director, IT Solutions Marketing, from Splunk a couple of weeks ago. It was a great chance for me to know more about Splunk and of course I had to ask him his thoughts on Big Data. He was kind enough to allow our conversation to be a part of my Big Data Conversation series. Splunk

A little background about Splunk though Jon’s voice… Splunk helps you make sense of machine data and machine data is the largest and fastest growing component of Big Data. The most under used data comes from the massive amounts of data from applications, devices, servers, network end points and are often under-used because of how difficult is can be to capture, store and analyze using outdated. Our Big Data story is about real-time machine data. We keep your systems up and running and we keep you more secure.

 

EB: How does your company define Big Data?

JR: Splunk wouldn’t define it differently then anyone else. We believe that the jumping off point to Big Data is volume, velocity, and variety. All the data that is too unwieldy to put in to traditional databases and is difficult to keep up with.  The business press would discuss Big Data with Hadoop and state it was all about dumping together all your e-commerce and company transactions, and develop sentiment analysis about what people wrote on Twitter and product reviews. This is the human generated part of Big Data but the machine generated part of Big Data is actually the bigger portion of that and the harder to manage at scale. If you look at what people were doing with that data like pattern recognition, you can do that through batch but we focus on real-time data. Yes, we have that historical piece but It is much more valuable to do while it is happening then doing it post-mortem which is the traditional way of doing it.

 

EB: Do you feel the majority of organizations associate Big Data with Hadoop?

JR: I don’t think our customers do but the broader business and tech media, in the past 6 – 8 years, use “how does Amazon know what to recommend you” and “how does the CDC know that there are flu infections based on what they see on twitter”. Those are examples that ground Big Data vs “how do you look at millions of transactions through an API end point to see response time”. These are also Big Data examples and what Splunk does.

 

EB: How do you see Big Data changing in the future?

JR: People over time, as it becomes normalized, will see the scale of “big” change. The goalpost on what “big” means will move. People will remove the requirement that it is Big Data if you can’t cleanly fit in to a relationship database. Right now if you have to put it in to a NoSQL database, it is Big Data but that is not necessarily true. Right now there is a tight coupling between NoSQL databases and Big Data and I think that will change just as architectures change. You need to have the solution fit your architecture better and not because it handles petabytes of data. It now becomes another storage strategy that isn’t solely driven by volume, velocity, and variety. There are other architectural considerations that can help you make a decision.

 

EB: What is the biggest myth about Big Data?

JR:There are a lot. One of them is that not many people have figured it out and that there are only a handful of businesses that are driven by Big Data. There is the myth that people over estimate the sophistication of analysis done in Big Data, everyone thinks that everyone is doing what Amazon is doing when instead people are doing simple correlations.

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