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Tips for Building Analytic Workloads

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Over the past 16 months, we’ve covered the new age of data / analytics from various perspectives. First, the explosion of enterprise data itself. Particularly unstructured data and how that’s radically changed the way companies must evolve their data strategies. We also examined the ideal data infrastructure architectures for analyzing all that data and delivering business value. Finally, we updated readers on current trends in enterprise analytics. Including cloud strategies and the move to open source analytics solutions.

It’s a lot to digest, but there’s one more data analytics topic to address: the essentials for building out your own workloads. Let’s dig in.

If your situation is like many enterprises across industries today, your data is in disarray. It’s likely spread across your environment in a mix of storage infrastructures (on-premises, cloud, edge, data lakes, data warehouses). Each one of a different vintage and using different protocols, APIs, and so on. Some of your data is currently unreachable, and much of it is not subject to the analysis that’s going to drive future insights and business transformation. Your objective now is to pull off data-centric modernization in a landscape of siloed data and multi-generational data infrastructure.

So, from such a starting point, how do we build out your modern analytics?

1. Solve for object storage on-prem

When considering your strategy for dealing with new, massive data sets, object storage is increasingly popular because it provides crucial advantages. No longer simply a cheap and deep archive for data, object storage is designed for the large volumes of data required to build, train, and manage analytic models. And, just as important, object storage is highly scalable, making it perfect for the large and unpredictable data volumes that analytics workloads must contend with.

There’s one other attribute that makes object storage unique: it’s industry-standard protocol. S3, is the lingua franca of the cloud, so you don’t have to be an expert to use it. Nor do the data engineers, data architects, and data scientists on your teams, because they’re already familiar with it. With an API-centric model that’s easy to use, object storage already aligns with your modernization efforts.

You’re most likely using object storage-based resources in the cloud. To bring greater agility and flexibility to your entire environment. It makes sense to enable an on-prem object storage solution. For that, you’ll need management software with the flexibility and consistency to deliver seamless on-prem and cloud operations, according to your business needs. By unifying your data operations in this way, you’re not just eliminating silos, you’re empowering teams enterprise-wide. From business intelligence analysts to SQL and Spark users, to machine learning data scientists — to accelerate. Ultimately that means faster time to value.

2. Leave yourself open to a wide variety of analytics tools

Open source solutions like Spark, Delta Lake, Livy, and Hive can add powerful analytic tools to your organization while also removing the risks of lock-in. Look for data platforms that offer integrations with the leading open source tools alongside a marketplace of partners who can further expand your options.

3. Integrating Kubernetes is a must

Although certain large, scale-out environments will inevitably remain on bare metal, container environments are now ubiquitous in analytics deployments. Highly portable and efficient, containers deliver unprecedented agility and speed across clouds. Organizations transforming today need to capitalize on the move to containers by leveraging an orchestrated Kubernetes environment that automates the provisioning and management of applications.

4. Leverage the flexibility of hybrid cloud

As you modernize, hybrid cloud becomes almost unavoidable. Whether you’ve made the move to hybrid cloud yet or not, you’ve probably already seen how the ability to deploy and migrate workloads across on-prem and public cloud based on performance, security, cost, and more can be invaluable. Now, to lock in those advantages, you need to ensure seamless app and data mobility across clouds via modern, edge-to-cloud data services capable of optimizing each and every workload.

The bottom line

Once you’ve addressed each of these considerations, you’re ready to build the analytics solution that will let you see around corners, drive innovation, and gain the advantages to power success into the future. With its industry-leading, as-a-service infrastructure solutions, HPE is already helping thousands of customers realize their goals.

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