High-Performance Spark Engine for Cost-Efficient Data Workloads

Traditional Spark engines weren't built for today's scale

Bill shocks from undersized clusters

Undersized clusters waste compute and create unpredictable, painful bill shocks at the end of every month.

Developer overhead

Complex interfaces slow teams down and add engineering overhead just to keep pipelines running.

Proprietary platform lock-in

Usage-based pricing on proprietary platforms locks teams in and punishes growth instead of rewarding it.

Key Features: What Makes Yeedu a High-Performance Spark Platform

Built to Run Parallel With Every Spark Platform, Cloud, and Catalog

Native integrations for orchestration, monitoring, cataloging, and AI services.

Enterprise-ready deployment in minutes

SOC 2 Type II equivalent security posture
Built on open-source Spark with enterprise-grade governance
Multi-cloud & hybrid support: AWS, Azure, GCP, Kubernetes
Zero vendor lock-in: everything remains portable and open

AWS EMR

Move high cost EMR workloads to Yeedu with no code rewrite

Fully supported

Databricks

Shift off usage-based licensing, run your critical pipelines 4–10× faster

Fully supported

Google Dataproc

Lower compute costs significantly

Fully supported

Cloudera

Migrate from legacy clusters to a modern, high-performance Spark engine

Fully supported

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

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. This capability allows us to optimize our cloud compute costs effectively compared to other available tools in the market, while consistently maintaining high performance standards.

Senior Director

Top 5 Healthcare Insurer

That's Why We Built Yeedu Differently

Read full benchmark & query results ➜

Turbo Rearchitectured Spark vs Traditional Spark

We benchmarked Yeedu's rearchitected Spark Turbo Engine against traditional Spark engines embedded in modern data platforms using real-world queries on a 1.5B-row dataset, across multiple clouds. The results show 4×–10× faster execution, up to 80% lower compute cost, and zero code changes required.

Read full benchmark & query results ➜

Yeedu Hits $0.53/TB in a Benchmark That Challenges Every Data Platform

Yeedu executed the full 99-query TPC-DS benchmark across 1TB, 3TB, and 10TB datasets with zero failures and no rewrites. Using right-sized Graviton4 instances, it delivers predictable performance without over-provisioning.