How to Maximize the Value Of Your Big Data Analytics Stack Investment
Big data analytics performance management is a competitive differentiator and a priority for data-driven companies. However, optimizing IT costs while guaranteeing performance and reliability in distributed systems is difficult.
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The complexity of distributed systems makes it critically important to have unified visibility into the entire stack. This webinar discusses how to maximize the business value of your big data analytics stack investment and achieve ROI while reducing expenses. Learn how to:
- Correlate visibility across big data applications and infrastructure for a complete and transparent view of performance and cost.
- Continuously tune your platform, and run up to 50% more jobs on Hadoop clusters.
- Optimally utilize resources, and ensure customer satisfaction.
- Simplify troubleshooting and problem resolution while resolving issues to meet SLAs.
In this webinar, learn specific ways to automatically tune and optimize big data cluster resources, recapture wasted capacity, and improve ROI for your big data analytics stack.
More on the episode:
So, as Dave mentioned, today we are talking about IT cost optimization. We'll explain a little bit about what that means and how, and what it does not mean, which is also very important in space.
And then, we'll talk a little bit about how to, or what kind of things you really should be doing to optimize your IT spend. So, let's go ahead and get into things. So, today's topic, we've already talked about is IT cost optimization. Let's read this Gartner definition.
I think it's important to keep in mind while we discuss everything, cost optimization is a business-focused, continuous discipline intended to maximize business value while reducing costs. I want to emphasize one part of there, we will be doing this a couple of times throughout this.
This is a continuous process. You don't, especially when you're dealing with large distributed systems like your big data environment, this is not something where you've tuned it once and it's now good. Your data set sizes are going to change. Your workloads are going to change, The technology being used on your platform is going to change. Optimization is a continuous process.
And what we're going to be talking about today is how to leverage observability, automatic tuning optimization to recapture wasting capacity and improve the return on investment for your big data analytics stack. So, let's go ahead and head on to our agenda slide here. We just went through the introduction.
Going to talk a little bit about the challenges. We're going to address some of those challenges. And then, we're going to show you a couple of examples of what we mean by optimization success. Summarize, and then any questions I do not get to during the presentation we will go through. So, what are the challenges to effective IT cost optimization? So, there are the very easiest high-level challenges. Distributed systems are complicated. There are a lot of moving parts. A lot of overlap of services. A lot of untracked interdependencies.
There's a lot going on and trying to sort out what you need to look at, and what you don't need to look at takes a lot of work. One thing to mention here, cost optimization is not the cost-cutting performance and reliability issues of the concurrent stack that can't be resolved by cutting your expenses.
What you really need to do is think about cost optimization and cost control. So, in order to eliminate waste, but also find ways to maximize the utilization of your spin. And as I said earlier, and we'll repeat several times, optimization is not a one-time action. It's definitely a lot longer than that. So, let's go ahead and start getting into this. What makes it difficult? Well, lack of visibility. That's going to be the biggest problem we see in most environments improper provisioning.
This could be a cluster that does not have the necessary resources to manage the workload that's being presented to it, or what we often find is it's a cluster that has been over-provisioned for how it's being utilized, or at least how it's been configured. Poor application architecture design and this isn't always even necessarily application architecture. It can be data architecture, as well...
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