When should I use Azkaban instead of Hamilton?

Hadoop ecosystem workflow scheduling with simple job dependency management via .job files. Teams with existing Hadoop infrastructure who need a lightweight, native scheduler. Simple DAG definitions without needing to write framework-specific Python or Java code

When should I use Hamilton instead of Azkaban?

Defining dataflow as pure Python functions where dependencies are resolved automatically by name. Feature engineering pipelines where lineage, documentation, and reusability across contexts matter. Teams who want to reuse the same function graph for training, serving, and batch processing

What are the main weaknesses of Azkaban?

Effectively legacy — active development has slowed significantly since LinkedIn open-sourced it. Web UI and feature set are dated compared to Airflow, Prefect, or Dagster. Tightly coupled to Hadoop; a poor fit for modern cloud-native or containerized pipeline architectures

What are the main weaknesses of Hamilton?

Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools. No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution. Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

Azkaban vs Hamilton: Key Differences for Python Data Engineering

Orchestration Tools

Azkaban

Hadoop Workflow Scheduler

★ 3.8

Apache-2.0

N/A — Java-based, no pip install

Hamilton

DAG-Based Data Transformation Library

★ 4.2

BSD-3-Clause

pip install sf-hamilton

Side-by-Side Comparison

Azkaban

Hamilton

Azkaban

Hamilton

Best For

✓Hadoop ecosystem workflow scheduling with simple job dependency management via .job files
✓Teams with existing Hadoop infrastructure who need a lightweight, native scheduler
✓Simple DAG definitions without needing to write framework-specific Python or Java code

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

Best For

✓Hadoop ecosystem workflow scheduling with simple job dependency management via .job files
✓Teams with existing Hadoop infrastructure who need a lightweight, native scheduler
✓Simple DAG definitions without needing to write framework-specific Python or Java code

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

Weaknesses

•Effectively legacy — active development has slowed significantly since LinkedIn open-sourced it
•Web UI and feature set are dated compared to Airflow, Prefect, or Dagster
•Tightly coupled to Hadoop; a poor fit for modern cloud-native or containerized pipeline architectures

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

Weaknesses

•Effectively legacy — active development has slowed significantly since LinkedIn open-sourced it
•Web UI and feature set are dated compared to Airflow, Prefect, or Dagster
•Tightly coupled to Hadoop; a poor fit for modern cloud-native or containerized pipeline architectures

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

License

Apache-2.0

BSD-3-Clause

License

Apache-2.0

BSD-3-Clause

Install

N/A — Java-based, no pip install

pip install sf-hamilton

Install

N/A — Java-based, no pip install

pip install sf-hamilton

Rating

★ 3.8

★ 4.2

Rating

★ 3.8

★ 4.2

Key Features

Azkaban

1Batch workflow scheduler originally developed at LinkedIn
2DAG-based job dependency management via properties files
3Web UI for workflow visualization, monitoring, and manual triggering
4HDFS and Hadoop ecosystem integration for data pipeline scheduling
5Email and SLA alerting on job failure and delay

Hamilton

1Micro-framework where Python function names define the data dependency DAG
2Functions with typed inputs and outputs automatically form a directed graph
3Executors: sync, async, Spark, Dask, and Ray for scalable execution
4Built-in lineage tracking and documentation generation from docstrings
5Feature engineering-focused with pandas, Polars, and numpy support

How Python Data Engineers Use These Tools

Azkaban

Python data engineers submit Azkaban workflow packages using the Azkaban REST API from Python scripts — uploading a ZIP of job definition files, triggering execution, and polling for completion status. Python jobs run as command-type Azkaban tasks that execute Python scripts on the cluster, with Azkaban managing the dependency ordering between pipeline steps.

Hamilton

Python data engineers use Hamilton to define feature engineering pipelines where each column or feature is a Python function whose parameters declare its dependencies. Hamilton builds the DAG from function signatures automatically — engineers add a new feature by writing a new function, and Hamilton wires it into the execution graph without any manual DAG configuration.

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Individual Tool Pages

View Azkaban details →View Hamilton details →

Side-by-Side Comparison

Azkaban

Hamilton

Azkaban

Hamilton

Best For

✓Hadoop ecosystem workflow scheduling with simple job dependency management via .job files
✓Teams with existing Hadoop infrastructure who need a lightweight, native scheduler
✓Simple DAG definitions without needing to write framework-specific Python or Java code

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

Best For

✓Hadoop ecosystem workflow scheduling with simple job dependency management via .job files
✓Teams with existing Hadoop infrastructure who need a lightweight, native scheduler
✓Simple DAG definitions without needing to write framework-specific Python or Java code

✓Defining dataflow as pure Python functions where dependencies are resolved automatically by name
✓Feature engineering pipelines where lineage, documentation, and reusability across contexts matter
✓Teams who want to reuse the same function graph for training, serving, and batch processing

Weaknesses

•Effectively legacy — active development has slowed significantly since LinkedIn open-sourced it
•Web UI and feature set are dated compared to Airflow, Prefect, or Dagster
•Tightly coupled to Hadoop; a poor fit for modern cloud-native or containerized pipeline architectures

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

Weaknesses

•Effectively legacy — active development has slowed significantly since LinkedIn open-sourced it
•Web UI and feature set are dated compared to Airflow, Prefect, or Dagster
•Tightly coupled to Hadoop; a poor fit for modern cloud-native or containerized pipeline architectures

•Small community with fewer tutorials, integrations, and Stack Overflow answers than mainstream tools
•No built-in scheduler — requires Airflow, Prefect, or a similar tool for recurring execution
•Mental model shift required: functions define graph nodes implicitly, not through explicit wiring

License

Apache-2.0

BSD-3-Clause

License

Apache-2.0

BSD-3-Clause

Install

N/A — Java-based, no pip install

pip install sf-hamilton

Install

N/A — Java-based, no pip install

pip install sf-hamilton

Rating

★ 3.8

★ 4.2

Rating

★ 3.8

★ 4.2

Key Features

Azkaban

1Batch workflow scheduler originally developed at LinkedIn
2DAG-based job dependency management via properties files
3Web UI for workflow visualization, monitoring, and manual triggering
4HDFS and Hadoop ecosystem integration for data pipeline scheduling
5Email and SLA alerting on job failure and delay

Hamilton

1Micro-framework where Python function names define the data dependency DAG
2Functions with typed inputs and outputs automatically form a directed graph
3Executors: sync, async, Spark, Dask, and Ray for scalable execution
4Built-in lineage tracking and documentation generation from docstrings
5Feature engineering-focused with pandas, Polars, and numpy support

How Python Data Engineers Use These Tools