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Kubeflow vs Apache Spark: What are the differences?
Developers describe Kubeflow as "Machine Learning Toolkit for Kubernetes". The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. On the other hand, Apache Spark is detailed as "Fast and general engine for large-scale data processing". Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Kubeflow and Apache Spark are primarily classified as "Machine Learning" and "Big Data" tools respectively.
Kubeflow and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than Kubeflow with 7.04K GitHub stars and 1.03K GitHub forks.
Pros of Kubeflow
- System designer5
- Customisation3
- Kfp dsl3
- Google backed2
Pros of Apache Spark
- Open-source58
- Fast and Flexible47
- One platform for every big data problem7
- Easy to install and to use6
- Great for distributed SQL like applications6
- Works well for most Datascience usecases3
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Interactive Query0
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Cons of Kubeflow
Cons of Apache Spark
- Speed2
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What is Kubeflow?
What is Apache Spark?
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