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Apache Spark vs Kubeflow: What are the differences?

Apache Spark and Kubeflow are both popular tools used in the field of big data processing and analytics. While they have some similarities in terms of their ability to process and analyze large datasets, there are also key differences between the two.

  1. Deployment Architecture: Apache Spark is primarily designed to run on a cluster, with the ability to scale horizontally by adding more worker nodes. On the other hand, Kubeflow is built on top of Kubernetes, which allows it to take advantage of the scalability and resource management capabilities of Kubernetes. This makes Kubeflow a more flexible option for deploying and managing machine learning workflows.

  2. Workflow Management: Apache Spark offers a comprehensive set of libraries and APIs for data processing and analytics, making it well-suited for complex data pipelines and batch processing. Kubeflow, on the other hand, focuses more on the orchestration and management of machine learning workflows, providing tools and frameworks for training, deploying, and versioning machine learning models.

  3. Machine Learning Ecosystem: While both Apache Spark and Kubeflow provide capabilities for machine learning, they have different approaches. Apache Spark integrates with popular machine learning libraries like TensorFlow and scikit-learn, allowing users to leverage these libraries within Spark. Kubeflow, on the other hand, provides a more integrated and end-to-end machine learning platform with built-in support for TensorFlow, PyTorch, and other popular ML frameworks.

  4. Development Flexibility: Apache Spark provides a high-level programming interface that allows users to write data processing and analytics applications in multiple languages such as Java, Scala, and Python. This makes it easier for developers with different language preferences to work with Spark. Kubeflow, on the other hand, is more focused on deploying and managing machine learning workflows rather than writing custom applications. It provides a set of pre-built components and operators that can be used to assemble and configure machine learning pipelines.

  5. Community and Support: Apache Spark has been around for several years and has a large and active community of developers and users. It has a mature ecosystem with extensive documentation, tutorials, and support resources available. Kubeflow, while gaining popularity, is relatively newer compared to Spark and may not have the same level of community and support. However, Kubeflow benefits from the thriving Kubernetes community and the growing interest in machine learning on Kubernetes.

  6. Use Cases: Apache Spark is often used for large-scale data processing, batch analytics, and ETL (Extract, Transform, Load) workflows. It is well-suited for scenarios where data needs to be processed and analyzed in parallel across multiple machines. Kubeflow, on the other hand, is more focused on machine learning workflows and is commonly used for tasks such as model training, hyperparameter tuning, and model deployment.

In Summary, Apache Spark is a powerful data processing and analytics framework geared towards parallel processing and batch analytics, while Kubeflow is a machine learning platform built on Kubernetes for managing and orchestrating machine learning workflows.

Advice on Kubeflow and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 531.5K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Replies (2)

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 373.5K views
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Pros of Kubeflow
Pros of Apache Spark
  • 9
    System designer
  • 3
    Google backed
  • 3
  • 3
    Kfp dsl
  • 0
  • 61
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation

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Cons of Kubeflow
Cons of Apache Spark
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    • 4

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    - No public GitHub repository available -

    What is Kubeflow?

    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.

    What is Apache Spark?

    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.

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    Mar 24 2021 at 12:57PM


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    What are some alternatives to Kubeflow and Apache Spark?
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).
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