Modern data platforms are evolving—and speed, scale, and efficiency are becoming non‑negotiable.

In this episode of Exchanges with Hitachi Solutions, host Matt Volke sits down with Evan Sotos, Engineering Manager for the Empower Data Platform, fresh off his return from NVIDIA GTC. Together, they explore how GPU acceleration is moving beyond AI and machine learning—and into the core of data engineering.

The conversation dives into what Evan heard from engineers, partners, and vendors at GTC, why NVIDIA is positioning itself as an algorithms company, and how technologies like NVIDIA RAPIDS are being used to dramatically accelerate analytics and data pipelines without rewriting existing code.

What You’ll Learn

· Why GPU acceleration is becoming a core capability for modern data platforms, not just AI workloads

· What NVIDIA RAPIDS is and how it enables existing CPU‑based workloads to run on GPUs

· How GPU acceleration can significantly reduce processing time and overall compute costs

· Why “zero code changes” is such a critical advantage for real‑world data teams

· Which types of data workloads benefit most from GPU‑accelerated pipelines

From AI Buzz to Real‑World Data Engineering Impact

While NVIDIA GTC is often associated with AI and large language models, this conversation highlights a broader shift: GPUs are increasingly being applied to traditional data engineering and analytics workloads.

Evan shares how NVIDIA RAPIDS acts as a mapping layer that allows existing Spark and Databricks workloads to take advantage of GPU compute. Rather than forcing teams to refactor complex, production‑grade code, GPU acceleration can be enabled through configuration—making it practical for teams to test, validate, and adopt without disruption.

The result? Faster pipelines, improved cost efficiency, and a shorter path from raw data to actionable insight—especially for large, time‑sensitive workloads.

What This Means for Data Teams

For organizations running large‑scale analytics, predictive models, or operational reporting, time truly is money. Evan explains how accelerating data pipelines can directly impact downstream use cases—from predictive maintenance to real‑time decision‑making—by reducing the lag between data ingestion and insight.

Most importantly, this episode emphasizes practicality: GPU acceleration isn’t about chasing hype. It’s about giving data teams another tool they can turn on, test, and adopt when it makes sense—without introducing risk, rework, or operational complexity.

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