Private Inference API
Accelerated Data

Spark was never meant to be this slow

Nebul runs Apache Spark on Analytics Processing Units (APUs), delivering up to 100× faster analytics by changing how Spark executes — without code rewrites.

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 changes how Spark workloads are executed by moving core analytics operations from general-purpose CPUs to dedicated analytics silicon. At the center of this approach are Analytics Processing Units (APUs) from Speedata, processors designed to execute Spark operators directly in hardware.

CPUs orchestrate Spark. APUs execute it.

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. The execution engine moves from software on CPUs to hardware acceleration, delivering massive speedups without rewriting applications.

From batch analytics to real-time insight

By executing analytics in hardware, Spark workloads complete in minutes or seconds instead of hours — transforming analytics from periodic batch jobs into near real-time decision systems.

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.

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Stop tuning Spark. Start getting realtime insights.

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