A Re-Architected High-Performance Compute Engine For Spark Workloads
4-10x faster execution with 50-80% lower compute spend, a new architecture open built for modern Spark.
What's Yeedu's Turbo Engine & Why Your Data Teams Need It
Yeedu Turbo Engine is a re-engineered C++ execution layer that preserves Spark compatibility while delivering vectorized, cache-optimized, SIMD-accelerated performance achieving 4–10x faster execution and up to 80% lower compute costs.
Optimized Execution for CPU-Bound Spark Workloads
Traditional Spark engines run into severe CPU bottlenecks during complex transformations, joins, and aggregations. Yeedu Turbo Engine offloads CPU-heavy operations to a C++ SIMD engine to eliminate bottlenecks.
- Accelerate execution by 4–10x with SIMD vectorization.
- Eliminate JVM garbage collection overhead for critical pipeline paths.
- Native C++ engine for column-wise operations and SIMD vector processing.
- 2-4x faster execution for CPU-intensive data transformations.
Engineered from the ground up for modern hardware architectures, Turbo Engine unleashes the full compute power of modern CPUs without requiring data teams to modify their existing Spark codebase.
A Modern Execution Engine Beneath A Familiar Spark Interface
Turbo Engine accelerates CPU-bound Spark workloads through a C++-based execution engine that implements SIMD processing, SIMD instructions, and offloads CPU-bound task execution. This downstream execution platform delivers 4–10x faster execution while reducing overall compute spend by 50–80%, all without requiring any changes to your Spark code.
Smart Scheduling: Maximum Efficiency for I/O-Bound Workloads
Traditional engines waste compute cycles waiting on I/O operations like reading from cloud storage or writing to metastores. Yeedu Smart Scheduling optimizes resource usage by multiplexing jobs and dynamically shifting workloads between nodes.
Data pipelines often face severe I/O bottlenecks. Yeedu solves this by intelligent task scheduling, pre-fetching data, and multiplexing jobs across shared cluster resources to ensure 100% compute utilization.
Engineered for multi-tenant environments, Smart Scheduling automatically balances job priorities, prevents noisy-neighbor issues, and maximizes overall cluster throughput.
Ready to benchmark Turbo Engine on your real workloads?
Benchmark your real-world Spark workloads in your own cloud environment with a 30-day proof of concept.
What Our Customers Are Saying
Rising data platform costs are among the biggest challenges enterprises face today. Yeedu provides a transformative solution that drastically reduces expenses and eliminates this critical barrier, empowering organizations to focus on driving innovation and achieving their goals.
Dr. Mark Ramsey
Ex-Chief Data Officer, GSK & Samsung Mobile
Traditional data platforms charge based on cloud resource consumption, such as the number of cores and hours used. The more you consume, the more they profit - leaving the problem of high cloud costs unaddressed. Using Yeedu turns their inertia into your benefit.
Milind Chitgupakar
Founder & CEO, Yeedu
We are thrilled to explore the vast possibilities that Yeedu introduces to our enterprise. With its cloud-agnostic compute management plane, Yeedu enables us to scale our computational tasks to the required dimensions efficiently while consistently maintaining high performance standards.
Senior Director
Top 5 Healthcare Insurer