Apache Spark vs Kubeflow

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Kubeflow

Stacks205

Followers585

Votes18

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.

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.
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.
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.
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.
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.
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.

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Advice on Apache Spark, Kubeflow

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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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Detailed Comparison

Apache Spark	Kubeflow
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 205
Followers 3.5K	Followers 585
Votes 140	Votes 18
Pros & Cons
Pros 61 Open-source 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 Cons 4 Speed	Pros 9 System designer 3 Kfp dsl 3 Customisation 3 Google backed 0 Azure
Integrations
No integrations available	Kubernetes Jupyter TensorFlow

What are some alternatives to Apache Spark, Kubeflow?

TensorFlow

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.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Related Comparisons

Apache Spark vs Kubeflow: What are the differences?

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.
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.
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.
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.
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.
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.

Apache Spark vs Kubeflow

Overview