How To Manage Big Data Analytics In The Cloud: Best Practices
Big data with cloud computing is a powerful combination that can transform your organization, process, and analyze your big data faster, and improve your products and business with actionable insights.
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Bringing your big data cluster to the cloud presents huge opportunities, but there are some challenges that need to be overcome. Is your organization really ready for the complexity of managing big data analytics in the cloud?
Most big data enterprises have either adopted cloud computing to improve IT operations and develop better software, faster, or they have the initiative to get there. Preparing for a successful move to the cloud is the difference between realizing the ROI the cloud promises, managing impatient stakeholders and SLAs, and possibly moving back to an on-premises solution.
Creating an accurate cloud footprint requires good planning, a deep understanding of resource utilization, and granular data. In this webinar, we’ll discuss how to prepare and ensure that your organization has a solid plan to manage big data analytics in the cloud.
– Primary characteristics of big data and putting your data in the cloud
– The challenges of managing big data performance in the cloud
– FinOps (chargeback, analyzing wasted spend)
– Planning for day 2
– Achieving cloud performance
– Observability, continuous tuning, and managed autoscaling
More on the episode:
So, today's webinar, as you mentioned, is Managing Big Data Analytics and Cloud-Are You Ready? So, we're going to go over a couple of things today. First, when we talk, when we're talking about big data in the cloud, what does that mean? What does big data mean? Why do we want to put it in the cloud?
Cover a little bit of the challenges of managing your big data performance and analytics stacks in the cloud. Briefly talk about fin-ops. And then, when we say planning for day two, we mean now you're in there, how are you going to move forward past this? Talk a little bit about cloud performance, observability.
And of course, we'll summarize everything and go over the Q&A. So, let's go ahead and start off. When we say big data, we like to say the old definition used to be pretty easy. Data was the size of it became a management problem. We all work in that world now.
So, really when we say big data, now we're talking about the volume of data we collect: the velocity at which the data is incoming. How fast can we process it and bring it in? The variety of data.
We're not just streaming numbers off in an IoT device. We're gathering in media posts. We're gathering on social media. We're gathering in IOT numbers. We're gathering all sorts of data.
And the veracity- we want to make sure that we're not just looking at noise, but we're actually using our analysis and our big data tools to find out what's useful and what's not. So, there's obviously a very strong case for putting big data in the cloud. It does require zero capital expenditures.
And if there's one thing big data means, it means spending a lot of money on hardware, data center space, power, cooling, all that other fun stuff. It allows you to scale faster. Instead of having to wait to obtain hardware and get it racked and stacked, get it online, get it ready.
You just turn on more instances in the cloud. Which, in theory, should lower the cost of analytics, encourage you to be a lot more agile. And because someone else is managing the problem.
And you can very easily replicate some of the stuff between clouds, between geographic locations within the cloud, or just use the vendor's tools. To do that continuity and disaster recovery become simplified.
Now, there are some challenges to this. First of all, this picture in the middle illustrates it nicely. There are a lot of tools and a lot of options out there. From a business side, you need to make sure that you cover your future growth trends, your cost projections, your SLA requirements, your data engineering, means to make sure that your apps run.
If the problem is the platform or your job, your IT operations need to learn a new cloud or learn how to scale in a different way than they have in the past...
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