Features
This page summarises the features Yeedu provides for two audiences: developers who build and operate Spark workloads, and platform owners who optimize their Spark infrastructure and control cost.
Job management features
Universal CLI
Yeedu ships a Command Line Interface (CLI) for Windows, Linux, and macOS. You can create, manage, and monitor jobs with a small set of intuitive commands — useful for both interactive work and CI/CD automation. See the CLI commands reference.
Logging
Yeedu provides comprehensive logging so you can monitor and debug Spark jobs in real time. Both standard and custom logging levels are available, so you can pick the verbosity that fits your workload.
Dependency management
Yeedu detects and installs the dependencies your Spark jobs require, and it ships pre-built dependencies for commonly used libraries. This removes the friction of bootstrapping the runtime so you can focus on the workload itself.
Apache History Server
Yeedu runs an Apache History Server so you can view and analyze the performance of completed Spark jobs — track progress, inspect detailed metrics, and identify bottlenecks.
Cluster monitoring
Yeedu offers detailed cluster monitoring with metrics and alerts for CPU, memory, and network — see Cluster monitoring and Node-level monitoring.
Platform features
Autoscaling
Clusters scale up and down with workload demand, so you only pay for the resources you are actively using. See [Load-based sizing](../Clusters/Load Based Sizing.md).
Auto-stop and auto-start
Yeedu can automatically stop and start clusters on a schedule or when specific conditions are met, which removes idle cost. See [Auto suspension](../Clusters/Auto Suspension.md) and [Suspend and resume clusters](../Clusters/Suspend & Resume Clusters.md).
Multi-version Spark support
Yeedu supports multiple versions of Apache Spark, so different teams can pin to the version that suits their workload — and you can upgrade at your own pace.
Billing breakdown
Detailed billing breakdowns let you track infrastructure spend per workspace, cluster, and job, and identify where to optimize. See the [usage dashboard](../Usage and Cost Management/UsageDashboard.md).
Notebook support
Notebooks are the primary surface for interactive data science and machine learning on Yeedu. They support multiple languages, collaborative editing, version control, and integrated visualizations. See [Notebooks](../Notebook/Create notebook.md).
CUDA support
Yeedu supports CUDA-enabled NVIDIA GPUs, so you can accelerate ML training and large-scale data processing.
Runtime job arguments
You can launch a job instance with custom configuration and runtime arguments to override defaults for a specific run, without changing the base job definition. See [Jobs](../Jobs/Create Job.md).
Git integration
Workspaces integrate with Git so you can version-control notebooks and job code alongside the rest of your repository. See Git operations.
AssistantX
AssistantX is the in-product AI assistant that helps you author code, explain results, and debug failures from inside the notebook editor. See AssistantX.