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The Convergence of High-Performance Computing and Data Analytics

High-performance data analytics is one of the new superheroes of business. In principle, the integration of big data analytics and high-performance computing (HPC) offers a compelling response to an all-too-common experience among data scientists. The feeling is perhaps best summarized as a simple cry for help: “I can’t let this model run forever – I need answers now!” To put it more dispassionately, data scientists across many sectors are struggling to manage their workloads. As big data becomes bigger, legacy analytics tools and grid IT infrastructures are no longer able to deliver results within required timeframes. But what’s creating the bottlenecks, and why is high-performance data analytics seen as the answer?


What’s Driving the Shift to High-Performance Data Analytics?

The momentum behind data science is relentless. The global big data analytics market is predicted to grow in value from $348 billion in 2024 to $924 billion in 2032. What’s more, organizations seeking to reap insight from data are unlikely to find themselves short of raw material. Digital transformation is fueling the flow of information in almost every area of business. Social media, the Internet of Things (IoT), chatbots, smart manufacturing, and customer relationship management (CRM) tools are just some of the resources that will generate ever-larger volumes of data that organizations can utilize. 

The growing buzz around high-performance data analytics isn’t simply a response to a fast-expanding data lake. Just as significant is the growing cohort of people utilizing data analytics in their day-to-day work. According to the U.S. Bureau of Labor Statistics, employment of data scientists will increase by 35% between 2022 and 2032. But “pure” data scientists are only part of the story. A new generation of low-code/no-code data analytics platforms is also empowering an emerging group of “citizen data scientists.” 

These citizen data scientists aren’t simply compensating for a shortfall in specialists. They represent a paradigm shift in how organizations are approaching and implementing data analytics. Leadership teams are recognizing that data analytics should be driven by people with granular understanding of the processes to which it’s applied. Domain expertise is vital to optimize outcomes. It ensures that models are based on the most detailed appreciation of the problems being addressed and the metadata being mined for insight. 

For many disciplines, data analytics adoption is a natural progression. Engineers, for example, are problem solvers by nature and typically have an aptitude for mathematics and data interpretation. They’re also drawn to tools that can deliver better outcomes faster, such as simulation, optimization, and data analytics. As a result, they’re one strand of a fast-growing population of citizen data scientists helping enterprises extract greater value from their data science investments. The flip side is that more people need access to computing resources to run their models.


The Perfect Marriage of Big Data Analytics And HPC

To understand where high-performance data analytics fits into the picture, it’s worth considering the typical data science project life cycle. Initially, there’s a focus on framing a business problem as a data analytics problem, and the challenge of gathering and preparing the associated data. Only then does the modeling work begin. But once a sound model is created, it’s a case of rinse and repeat. Good data science is the product of asking questions over and over again to improve the model’s accuracy and robustness. That means data scientists incur repeated model run time as they inch closer to the required answer. 

Access to sufficient computing resources is therefore critical to the quality of a data scientist’s output. If they lack the capacity to use all their data or try all possibilities, model quality suffers. Moreover, for hard-pressed data science teams, the light at the end of the tunnel is always an oncoming deadline. They can’t let that model run forever. Something needs to go out the door. 

Traditionally, data science platforms haven’t provided direct access to HPC. This is probably the legacy of an era in which data analytics had a much lower profile and was less well-integrated with other areas of business. Now, that’s changing. Low-code/no-code tools such as Altair® RapidMiner® are characterized not just by their accessibility to non-specialist users, but also the convergence of big data analytics and HPC. As a result, Altair RapidMiner offers new opportunities to overcome the headwinds and friction that can hamper the delivery of high-quality outputs. Efficient HPC — robust infrastructure, optimized workload management, and streamlined processes — is the cornerstone for harnessing the potential of new users and analyses across various sectors within organizations. Enterprises rely on technology like Altair® HPCWorks® to manage IT complexity and enable the latest artificial intelligence (AI) workloads with flexible, scalable scheduling and workflow design.


Balancing the Load

Some of the key benefits of high-performance data analytics are obvious. Data scientists can run their models faster. It’s easier to explore more possibilities and still meet deadlines. But the advantages of combining big data analytics and HPC also ripple outwards, reaching numerous stakeholders. That’s because HPC enables organizations to manage and access their computing resources with far greater flexibility, dynamism, and efficiency.  

Once again, consider the data scientist’s typical workload. Generally, most time is expended on routine tasks such as data and feature preparation. Here the strength of HPC lies in the fact that it represents a range of cloud-based CPU and GPU resources. Furthermore, these resources can be accessed and allocated when and where they’re most needed and most appropriate. Workloads such as data prep can be handled by CPUs, leaving GPUs free for tasks that justify the additional cost. 

HPC’s inherent flexibility provides benefits that extend across the organization. HPC smooths the peaks and troughs, ensuring everyone has access to resources that match their requirements. Intelligent workload management facilitates effective task prioritization. For example, if delays in a project will have serious consequences elsewhere in the organization, there’s a clear logic in ensuring it’s given priority access to resources that will expedite the job as quickly as possible. Equally, organizations that utilize HPC aren’t paying for idle IT infrastructure when their demand for resources is low. 

The growth of low-code/no-code platforms that marry big data analytics and HPC promises to transform work in any number of disciplines. However, the democratization of data science also needs to be part of another equally powerful trend: convergence. To flourish within a constantly changing landscape, enterprises need to break the boundaries between disciplines and technologies. The convergence of data analytics and HPC is powering this revolution, ensuring that citizen data scientists’ enthusiasm to leverage accessible analytics tools isn’t frustrated by computing bottlenecks. At the same time, high-performance data analytics gives specialist data scientists the freedom to concentrate on tasks that contribute most to the business. With collaboration more important than ever, HPC brings teams together. And in a world in which organizations need to extract meaningful insight more quickly, high-performance data analytics is proving to be the right answer to the right question, a business superhero we can believe in. 

To learn more about Altair’s data analytics and AI capabilities, visit To learn more about Altair’s HPC and cloud computing capabilities, visit