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Apache Oozie VS Kedro

Compare Apache Oozie VS Kedro and see what are their differences

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Apache Oozie logo Apache Oozie

Apache Oozie Workflow Scheduler for Hadoop

Kedro logo Kedro

An open-source framework for data science code
  • Apache Oozie Landing page
    Landing page //
    2021-07-25
Not present

Apache Oozie videos

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Migrating Apache Oozie Workflows to Apache Airflow

More videos:

  • Review - Breathing New Life into Apache Oozie with Apache Ambari Workflow Manager
  • Review - Breathing New Life into Apache Oozie with Apache Ambari Workflow Manager

Kedro videos

No Kedro videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Apache Oozie and Kedro)

Apache Oozie

Kedro

Workflow Automation
89 89%
Workflow Automation
11% 11
Data Pipelines
0 0%
Data Pipelines
100% 100
IT Automation
100 100%
IT Automation
0% 0
DevOps Tools
100 100%
DevOps Tools
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Oozie and Kedro

Apache Oozie Reviews

10 Best Airflow Alternatives for 2024
One of the workflow scheduler services/applications operating on the Hadoop cluster is Apache Oozie. It is used to handle Hadoop tasks such as Hive, Sqoop, SQL, MapReduce, and HDFS operations such as distcp. It is a system that manages the workflow of jobs that are reliant on each other. Users can design Directed Acyclic Graphs of processes here, which can be performed in...
Source: hevodata.com

Kedro Reviews

5 Airflow Alternatives for Data Orchestration
Kedro provides a standardized project template, data connectors, pipeline abstraction, coding standards, and flexible deployment options, which simplify the process of building, testing, and deploying data science projects. By using Kedro, data scientists can ensure a consistent and organized project structure, easily manage data and model versioning, automate pipeline...
Source: www.kdnuggets.com
10 Best Airflow Alternatives for 2024
Add CLI Commands: Plugins can be used to insert extra CLI commands that will be reused across projects. Kedro plugins allow you to extend Kedro’s functionality and inject new commands into the CLI. Plugins are created as stand-alone Python packages that are explicit to any Kedro project.
Source: hevodata.com
Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Kedro enables you to define pipelines using list of node functions with 3 arguments (func: task processing function, inputs: input data name (list or dict if multiple), outputs: output data name (list or dict if multiple)) in Python code (an independent Python module).
Source: medium.com

Social recommendations and mentions

Based on our record, Apache Oozie seems to be more popular. It has been mentiond 1 time since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Oozie mentions (1)

Kedro mentions (0)

We have not tracked any mentions of Kedro yet. Tracking of Kedro recommendations started around Mar 2024.

What are some alternatives?

When comparing Apache Oozie and Kedro, you can also consider the following products

Control-M - Control‑M simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

ActiveBatch - Orchestrate the entire tech stack with ActiveBatch Workload Automation & Job Scheduling. Build and manage workflows from one place.

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

Stonebranch - Stonebranch builds IT orchestration and automation solutions that transform business IT environments from simple IT task automation into sophisticated, real-time business service automation.

Dagster - The cloud-native open source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.

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