Azkaban vs Apache Airflow
Side-by-side — pricing, strengths, tradeoffs, and fit.
Azkaban
Batch workflow job scheduler for Hadoop jobs.
Apache Airflow
A platform to programmatically author, schedule, and monitor workflows.
At a glance
Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
● Established·From N/A
Overview
One-liner
One-liner
Batch workflow job scheduler for Hadoop jobs.
One-liner
A platform to programmatically author, schedule, and monitor workflows.
Maturity
Maturity
DecliningMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableStarts at
Starts at
$0
Starts at
N/A
Model
Model
Flat rate
Model
N/A
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams needing to schedule and manage large numbers of Hadoop jobs efficiently
Organizations with a significant investment in the Hadoop ecosystem looking for workflow management solutions
✓ Best for
Data engineering teamsProvides robust tools for orchestrating complex ETL workflows.
Organizations with diverse data sourcesExtensible architecture supports integration with various systems.
Teams needing a visual workflow interfaceThe web-based UI offers clear visibility into task execution.
Not ideal for
✕ Not for
Projects that require real-time data processing or streaming capabilities
Developers who prefer cloud-based managed services over self-hosted solutions
✕ Not for
Small teams with limited Python expertiseThe complexity of setup and DAG creation can be a barrier.
Real-time data processingAirflow's batch-oriented design is not suited for real-time tasks.
Strengths
↑ Strengths
Easy-to-use web interface for managing workflows
Supports complex job dependencies and scheduling
Integration with Hadoop ecosystem
↑ Strengths
Highly extensible with plugins
Active community and development
Web-based UI for monitoring
Open-source and free to use
Weaknesses
↓ Weaknesses
Limited support for non-Hadoop ecosystems
Complex setup process
Limited documentation and community support
Performance issues at scale
↓ Weaknesses
Complex setup and configuration
High resource consumption