Ray vs Apache Spark
Developers should learn Ray when building scalable machine learning or data-intensive applications that require distributed computing, such as training large models, running hyperparameter sweeps, or deploying AI services meets developers should learn apache spark when working with big data analytics, etl (extract, transform, load) pipelines, or real-time data processing, as it excels at handling petabytes of data across distributed clusters efficiently. Here's our take.
Ray
Developers should learn Ray when building scalable machine learning or data-intensive applications that require distributed computing, such as training large models, running hyperparameter sweeps, or deploying AI services
Ray
Nice PickDevelopers should learn Ray when building scalable machine learning or data-intensive applications that require distributed computing, such as training large models, running hyperparameter sweeps, or deploying AI services
Pros
- +It is particularly useful for teams transitioning from single-node to distributed setups, as it abstracts away cluster management complexities and integrates with popular ML frameworks like TensorFlow and PyTorch
- +Related to: distributed-computing, machine-learning
Cons
- -Specific tradeoffs depend on your use case
Apache Spark
Developers should learn Apache Spark when working with big data analytics, ETL (Extract, Transform, Load) pipelines, or real-time data processing, as it excels at handling petabytes of data across distributed clusters efficiently
Pros
- +It is particularly useful for applications requiring iterative algorithms (e
- +Related to: hadoop, scala
Cons
- -Specific tradeoffs depend on your use case
The Verdict
These tools serve different purposes. Ray is a framework while Apache Spark is a platform. We picked Ray based on overall popularity, but your choice depends on what you're building.
Based on overall popularity. Ray is more widely used, but Apache Spark excels in its own space.
Disagree with our pick? nice@nicepick.dev