Today’s data engineers are tasked with a monumental challenge: building and maintaining pipelines to analyze petabytes of data, often under intense cost and performance pressures. Traditional CPU-based infrastructures were never designed for this scale, and as CPU performance falls off, yesterday’s systems are quickly becoming today’s bottlenecks.

At Voltron Data, we believe GPU-accelerated analytics isn’t just faster — it’s transformational for your business, allowing you to accomplish previously impossible analytical tasks while slashing your cloud costs. Here are the top five challenges large-scale data pipelines face today, and how accelerator-native solutions like Theseus are unlocking the next era of innovation.

1. Massive Query Latency

The Challenge:

As data query sizes balloon past terabytes into petabytes, query runtimes stretch from minutes to hours — and even days. Waiting for large complex joins, aggregations, or filters to complete cripples SLAs and downstream workflows. Time is money, and lengthy query execution times carry an unavoidable opportunity cost, either by consuming resources that could be used productively elsewhere or by making you wait for the analytics you need.

How GPUs Help:

A CPU may have anywhere from four to 128 cores designed for single-threaded execution. A server-class GPU has several thousand general-purpose cores designed for parallel execution. Software written to leverage GPU parallelism, like Theseus, maximizes query execution across all of these general-purpose cores, delivering up to 100x faster performance when compared to CPU-based systems. Large-scale joins and aggregations complete in minutes, not hours, for significantly less cost.

2. Skyrocketing Infrastructure Costs

The Challenge:

Scaling CPU clusters to handle massive data volumes requires thousands of nodes, leading to ballooning costs in both hardware and energy consumption. That’s because CPUs aren’t designed for heavily parallel processing, and so you need significantly more of them to tackle the biggest data tasks.

How GPUs Help:

With Theseus, organizations can replace 200+ CPU nodes with just a handful of GPU nodes, achieving up to 99% cost savings on heavy analytics workloads, while also vastly reducing the time it takes for queries to complete. Fewer servers also mean lower maintenance overhead, data center footprint, and environmental impact.

3. Bottlenecks in AI/ML Pipelines

The Challenge:

AI models are only as good as the data behind them. Yet preparing petabyte-scale datasets for feature engineering and training can create massive bottlenecks, delaying model delivery and iteration cycles.

How GPUs Help:

Theseus reduces the time-to-market for AI applications by seamlessly unifying data analytics and AI/ML pipelines on the same GPU infrastructure. The same hardware powering AI model training and inference can be used for data processing. Data engineers can accelerate feature extraction, transformations, and preprocessing tasks — feeding models faster and enabling real-time machine learning applications like RAG (Retrieval-Augmented Generation) and deep learning, slashing development time and complexity.

4. Fragmented, Inflexible Architectures

The Challenge:

Legacy big data systems often lock teams into rigid architectures that make it hard to adopt new tools, optimize performance, or pivot as technology evolves. Many legacy systems lack native support for GPU-based analytics.

How GPUs Help:

Theseus is built for homogeneous and composable data architectures based on open standards like Apache Arrow, RAPIDS, and Ibis. If it supports one of the major dataframe standards or provides API access, you can use it with Theseus. With Theseus, you can enjoy faster and cheaper data processing without the need to rip and replace your entire infrastructure. This flexibility empowers engineers to integrate seamlessly with cloud data lakes (like Iceberg or Parquet) and evolve their infrastructure over time without being constrained by vendor lock-in or changing tooling.

5. Inefficient Raw Data Processing

The Challenge:

High-performance data analytics systems require extensive indexing, preprocessing, or warming up data before analysis starts, adding significant overhead to every analytics cycle.

How GPUs Help:

Theseus enables direct querying of raw data from remote object storage — No indexing required. This would otherwise be costly or tedious using a traditional CPU-based architecture, allowing engineers to analyze structured and semi-structured data immediately and eliminating unnecessary complexity in the pipeline.

The Future of Large-Scale Data Pipelines Is GPU-Powered

Data engineers are at the forefront of the next wave of innovation, but only if infrastructure performance keeps pace with data growth. We’ve reached a point where traditional CPU-based architectures aren’t enough to meet the data processing demands of today’s enterprises. GPU-accelerated solutions like Theseus deliver the speed, scalability, and flexibility needed to move beyond the bottlenecks of CPU-bound systems — while also slashing operational costs.

Theseus: GPU-Optimized Data Processing for Petabyte-scale Workloads

Voltron Data’s Theseus is Voltron Data’s high-performance query engine, purpose-built to leverage the NVIDIA GPU ecosystem. It enables data teams to:

• Accelerate query speeds up to 20x faster than Presto or Spark
• Reduce SLAs for massive datasets
• Lower compute and energy costs – by processing more data with fewer resources
• Seamlessly integrate with current data tools via Apache Arrow
• Write UDFs and enjoy total flexibility
• Use the tech stack and infrastructure resources they already have

Whether deployed on-premises or in cloud environments, Theseus allows data teams to do more with their existing data infrastructure, working seamlessly with AI and ML use cases.

If you’re ready to unlock real-time, petabyte-scale analytics and fuel AI innovation at scale, then it’s time to rethink your data pipeline foundation.

Test drive Theseus or schedule a demo at https://voltrondata.com/theseus.