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Compare Apache Airflow and python celery 
At a Glance
Market Segments
Mid-Market (47.1% of reviews)
Small-Business (57.1% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Apache Airflow
Star Rating
(88)4.3 out of 5
Market Segments
Mid-Market (47.1% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Apache Airflowpython celery
Star Rating
(14)4.6 out of 5
Market Segments
Small-Business (57.1% of reviews)
Entry-Level Pricing
No pricing available
Learn more about python celerySummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Apache Airflow excels in its ability to handle complex workflows with its Directed Acyclic Graph (DAG) feature, allowing for clear visualization and management of task dependencies, while Python Celery is often noted for its simplicity in handling straightforward task queues.
- Reviewers mention that Apache Airflow's scheduling capabilities are robust, with features like dynamic pipeline generation, which is particularly beneficial for data engineering tasks, whereas users say that Python Celery shines in its ease of use for background task processing, making it a preferred choice for smaller applications.
- G2 users highlight that Apache Airflow has a steeper learning curve due to its extensive features and configurations, with some reviewers noting that the setup process can be challenging, while users on G2 report that Python Celery offers a more user-friendly setup experience, making it accessible for developers new to task management.
- Reviewers mention that the community support for Apache Airflow is strong, with a wealth of plugins and integrations available, which enhances its functionality, while users report that Python Celery, although having a smaller community, provides excellent documentation that helps users troubleshoot effectively.
- Users say that Apache Airflow's performance in handling large-scale data workflows is impressive, with many reviewers praising its scalability, while users on G2 indicate that Python Celery is more suited for smaller, less complex applications, which may limit its scalability in larger environments.
- Reviewers mention that Apache Airflow's product direction is highly rated, with a positive outlook on future updates and features, while users report that Python Celery's development pace is slower, which may affect its long-term viability for users looking for continuous improvements.
Pricing
Entry-Level Pricing
Apache Airflow
No pricing availablepython celery
No pricing availableFree Trial
Apache Airflow
No trial information availablepython celery
No trial information availableRatings
Meets Requirements
9.1
60
9.5
13
Ease of Use
8.4
60
8.7
13
Ease of Setup
7.8
24
Not enough data
Ease of Admin
8.7
19
Not enough data
Quality of Support
7.8
55
8.8
10
Has the product been a good partner in doing business?
8.5
18
Not enough data
Product Direction (% positive)
9.6
56
8.1
12
Categories
Categories
Shared Categories
Apache Airflow and python celery are categorized as Other Development
Unique Categories
Apache Airflow has no unique categories
python celery has no unique categories
Reviews
Reviewers' Company Size
Apache Airflow
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
python celery
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Information Technology and Services
23.5%
Computer Software
16.5%
Financial Services
9.4%
Retail
3.5%
Marketing and Advertising
3.5%
Other
43.5%
Internet
35.7%
Computer Software
35.7%
Information Technology and Services
21.4%
Financial Services
7.1%
‐
Other
0.0%
Most Helpful Reviews
Apache Airflow
Most Helpful Favorable Review
Verified User in Marketing and Advertising
Apache Airflow is a great flexible tool that allows the data engineers/scientists to design data-intensive workflows efficiently. Airflow workflows are designed as DAGs (Directed Acyclic Graphs), each node of the graph can be anything (e.g. Python or bash...
Most Helpful Critical Review
Verified User in Hospital & Health Care
Can be a little difficult to get use to.
python celery
Most Helpful Favorable Review
Martin Thorsen R.
Verified User in Computer Software
The most helpful part of Celery is that it can use a plethora of different backends for distributing and coordinating tasks. For example, Celery can use RabbitMQ, Redis, or Amazon SQS as brokers and backends. Through SQLAlchemy, it can also interface with...
Most Helpful Critical Review
Verified User in Marketing and Advertising
Apache Airflow is a great flexible tool that allows the data engineers/scientists to design data-intensive workflows efficiently. Airflow workflows are designed as DAGs (Directed Acyclic Graphs), each node of the graph can be anything (e.g. Python or bash...
Martin Thorsen R.
Verified User in Computer Software
The most helpful part of Celery is that it can use a plethora of different backends for distributing and coordinating tasks. For example, Celery can use RabbitMQ, Redis, or Amazon SQS as brokers and backends. Through SQLAlchemy, it can also interface with...
Verified User in Hospital & Health Care
Can be a little difficult to get use to.
Discussions
Who is using Apache airflow?
1 comment
FE
Custom metadata to Atlan.Read more
Is Apache airflow an ETL tool?
1 comment
BM
Yes, we can use it to schedule and trigger ETL jobs and orchestrate them into a logical workflow Read more
python celery has no discussions with answers
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