One Spark Job. Any Cloud. Consistent Speed. Fixed Costs.
Modern data teams are juggling three big priorities all at once. They want portability - the freedom to run the same Spark job across any cloud environment without rewriting code. They want predictability - knowing in advance how long jobs will run, how resources will behave, and what the costs will be like. And they want control - to manage, monitor and govern everything from one single place, rather than stitching together multiple consoles and tools.
Yeedu delivers exactly that with a unified control plane for cross-cloud Spark execution. Whether you’re testing AWS, deploying Azure or scaling Google Cloud, you do it all without changing your code, without inflating your number of workspaces, and without being surprised by runaway costs. With Yeedu, you bring together cloud freedom, consistent execution and one stop operations - so your team can focus on delivering data-driven insights, not wrestling infrastructure.
To support this, Yeedu naturally improves and simplifies cross-cloud Spark performance tuning and cluster management, and provides architectural consistency required for Spark based cloud computing at enterprise scale.
With Yeedu you define a Cloud Environment for each provider - pick the region, set up credentials, your VPC/VNet, sub-nets and tags. Then at runtime you simply select that environment. The Spark clusters spin up inside your own network under your policies, keeping placement, IAM access and governance to stay exactly where you want them.
Three simple steps:
This setup provides consistent, controlled cloud Spark job optimization without introducing drift.
Together, these design choices support repeatable, measurable performance patterns - ideal for ongoing Spark based cost optimization efforts.
Yeedu’s built-in Turbo engine is an execution acceleration layer designed to speed up compute-heavy Spark/SQL workloads without code changes. Recent coverage reports 4–10× faster execution and ~60% lower costs, underscoring how acceleration plus orchestration can compound benefits across clouds. And it’s included in your plan (no add-on license needed). (PR Newswire)
Turbo acts as an intelligent complement to cross-cloud Spark performance tuning techniques, amplifying speed gains without introducing new operational overhead.
These controls simplify complex cross-cloud pathways and strengthen overall Spark cluster management, even at large tenant and workload scales.
1. Portability: Move the same Spark job to the cloud that best fits cost, data gravity, or latency no code changes.
2. Predictability: Consistent runtime behavior across providers, faster restarts with Warm Start, and fixed tier pricing (YCUs).
3. Visibility: Real-time usage and spend with granular filters (tenant, cluster, machine type, provider), plus unified logs and states.
These directly improve your ability to apply Spark performance optimization practices across clouds without fragmentation.
Because clusters run in your network, you retain your cloud-native controls (VPC/VNet policies, tags, identity). Yeedu’s control plane focuses on consistent execution and management, while your cloud accounts remain in the system of record for security baselines.
If you’re still maintaining separate workspaces or re-deploying the same job per cloud, you’re trading speed for overhead. With Yeedu, you can:
Taken together, this gives your team, end-to-end cloud Spark job optimization that works across providers, pipelines, and environments without refactoring or replatforming.
