Private Inference API
Accelerated Data

Spark was never meant to be this slow

Nebul partners with Speedata to run Apache Spark on their Analytics Processing Units (APUs) – the world’s first processors purpose-built for data analytics – delivering up to 100× faster performance without code rewrites.

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The challenges of
running Spark today

01

CPU-bound
execution

Most Spark workloads are limited by CPU processing. Systems spend more time moving, sorting, and transforming data than executing analytics efficiently.

02

Inefficient
scaling

Scaling Spark means adding more CPU nodes, increasing cost, power consumption, and operational complexity without addressing the core performance bottleneck.

03

Tuning
over insight

Teams spend weeks tuning clusters, memory settings, and shuffle behavior just to achieve acceptable runtimes, slowing down insight delivery.

04

Runaway
costs

As data volumes grow, long-running Spark jobs require ever-larger clusters, turning analytics into an expensive and periodic process instead of a real-time capability.

The problem isn’t Spark. It’s running it on hardware never designed for data analytics.

Rethinking how Spark runs

Spark was never meant to execute on CPUs. Nebul Accelerated Spark, powered by our partner Speedata changes how Spark workloads are executed by moving core analytics operations from general-purpose CPUs to dedicated analytics silicon. Speedata’s Analytics Processing Units (APUs) are purpose-built processors that execute Spark operators directly in hardware, the first silicon designed exclusively for data analytics.

CPUs orchestrate Spark. APUs execute it.

Accelerated Spark,
executed in hardware

CPUs were never designed to execute analytics at scale. APUs were.

Eliminate CPU driven inefficienies

APUs offload Spark’s most expensive operations; joins, aggregations, sorting, filtering from CPUs to dedicated analytics silicon, removing the primary bottleneck in traditional Spark clusters.

Change execution model, not framework

Spark APIs, jobs, and pipelines remain unchanged. Speedata's APU moves Spark execution from software on CPUs to hardware acceleration, delivering massive speedups without rewriting applications.

From batch analytics to real-time insight

By executing analytics on Speedata's purpose-built silicon, Spark workloads complete in minutes or seconds instead of hours, transforming analytics from batch jobs into near real-time decision systems.

What Accelerated
Spark delivers

The direct outcomes of executing Spark analytics in hardware.

Extreme
performance

Spark analytics run up to 100× faster, with complex queries and transformations completing in seconds or minutes instead of hours through hardware acceleration.

Reduced
infrastructure

Spark clusters require far fewer servers, CPUs, and memory, significantly lowering infrastructure footprint, power consumption, and operational overhead.

Real-time
insights

Analytics shift from periodic batch processing to near real-time insights, allowing operational and decision systems to respond immediately.

Spark
compatible

Runs standard Apache Spark APIs without proprietary rewrites, lock-in, or changes to existing pipelines and workflows, keeping existing Spark applications fully intact.

Sovereign
deployment

Deploy in Nebul’s sovereign NeoCloud, on-premises, or at the edge while maintaining data locality, control, and regulatory compliance.

Nebul
operated

System integration, lifecycle alignment, and operations are handled by Nebul, enabling teams to focus on analytics instead of platform maintenance.

Client case

The next era of AI in oncology with sovereign supercomputing

Kaiko.ai, a pioneering health tech scaleup, is transforming oncology care by bringing frontier AI into the hands of clinicians.

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Stop tuning Spark. Start getting realtime insights.

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