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Designing Infrastructure to Leverage Compute and Storage for Data Analytics

By Jason Zeng and Bryan Gilcrease | Posted on September 10, 2020 | Posted in Featured, Enterprise Infrastructure, Data & Analytics

In the past five years alone, advanced analytics have been massively adopted across industries as businesses push to make smarter decisions more rapidly.

In order to capture insights from advanced analytics, though, you need to have the right infrastructure in place to leverage an ever-growing amount of data. That means investing in both storage and compute.

Download now: Putting Artificial Intelligence to Work: A Guide to Designing High-Performance Data Infrastructure for AI Workloads

Preparing your infrastructure for storage and compute workloads

Seamless data collection and storage requires groundwork. The essential components of storage infrastructure are servers, storage systems, and storage area networks, which can either be virtual or physical. It’s up to you to decide whether you want to store your data in a traditional on-premises warehouse or in the cloud. 


To determine which option is the right fit for your organization, take stock of your compliance and security needs, as well as how much data you have access to. The cloud usually offers a cheaper price tag and makes it easy to scale without friction. However, if your enterprise deals with sensitive or highly regulated data—or if you have such a large pool of data that cloud access isn’t cost-effective—on-premises storage may be the better solution. 

Advanced analytics is also compute-intensive. These workloads may require you to invest in higher-capacity equipment and more of it. 

Engineering your strategy for infrastructure

When it comes to creating your game plan for infrastructure, begin by assessing where you currently stand. 

If you’re starting from scratch, isolate a specific use case that can be tackled with a small pilot program. By starting small, you can prove the value of the technology, get buy-in from decision makers in your organization, and then expand your infrastructure to address more demands.


If you’ve already adopted advanced analytics, your needs will look different. Now is the time to focus on scaling out your operation. Consider what resources will be needed as you grow and how you will manage those resources. 

Say, for example, you know you’ll eventually want to put in place an ETL process to load data into a warehouse. As you develop your infrastructure, you will need to anticipate and design around this eventual goal.

Building beyond architecture

At Redapt, our approach to building advanced analytics infrastructure goes beyond the architecture itself.

We work with you to evaluate all your current workflows, available talent, available resources, and your future needs. We also do a thorough deep dive into your current data resources, existing governance and security protocols, and storage.

The findings from this evaluation are then applied toward making an infrastructure design recommendation that fits both your current needs and where you want to see your organization in the future.

Is your organization ready to design infrastructure for AI and cloud-native workloads? Download our free eBook to learn more.

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Putting Artificial Intelligence to Work: A Guide to Designing High-Performance Datacenter Infrastructure for AI Workloads

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