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Apache Airflow

Apache Airflow

Overview

What is Apache Airflow?

Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s…

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Learn from top reviewers

One Stop solution for all the Orchestration needs.

Rating: 10 out of 10
July 7, 2025
IncentivizedVetted ReviewVerified User
I am part of the data platform team, where we are responsible for building the platform for data ingestion, an aggregation system, and the compute engines. Apache Airflow is o...
Continue reading
Raghwendra Singh
Engineer - Information Technology
SDE 4
Meesho
Apache Airflow6 years of experience

Scalable Scheduling Framework and Orchestration tool

Rating: 9 out of 10
May 30, 2025
IncentivizedVetted ReviewVerified User
We are using Apache Airflow as an orchestration tool in data engineering workflows in gaming product. We are scheduling multiple jobs i.e hourly / daily / weekly / monthly. ...
Continue reading
Anshuman Varshney
Engineer - Engineering
Engineering Manager
Gameskraft
Apache Airflow6 years of experience

Apache Airflow for Startups

Rating: 8 out of 10
December 18, 2024
IncentivizedVetted ReviewVerified User
Used Airflow for Analytics & Reporting
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Alok Pabalkar
Team Lead - Research & Development
Co-Founder & CTO
Mini Venture Lab Private Limited
Apache Airflow1 year of experience

Apache AirFlow - Love the Features, Love the Reliability.. Love if the UI get modenized!

Rating: 7 out of 10
June 26, 2022
IncentivizedVetted ReviewVerified User
We use apache airflow as part of our DAG scheduler and health monitoring tool. It serves as a core component in ensuring our scheduled jobs are run, the ability to allow us to...
Continue reading
Victor Tay
Engineer - Product Management
Engineer
IronNet CyberSecurity
Apache Airflow1 year of experience

A very nice job scheduler of DAGs that could become even better

Rating: 7 out of 10
June 13, 2022
IncentivizedVetted ReviewVerified User
We use apache Airflow in GCP as part of Cloud Composer to run all our ETL jobs.
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Nic Georgakopoulos
Manager - Engineering
DevOps
Deloitte Digital
Apache Airflow3 years of experience
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Pricing

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Apache Airflow

Apache Airflow

N/A
Unavailable

What is Apache Airflow?

Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top…

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  • No setup fee

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Product Demos

Getting Started with Apache Airflow

YouTube

Apache Airflow | Build your custom operator for twitter API

YouTube
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Features

Workload Automation

Workload automation tools manage event-based scheduling and resource management across a wide variety of applications, databases and architectures

9.2
Avg 8.4
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Product Details

What is Apache Airflow?

Apache Airflow Video

What's coming in Airflow 2.0?

Apache Airflow Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.

Reviewers rate Multi-platform scheduling highest, with a score of 10.

The most common users of Apache Airflow are from Enterprises (1,001+ employees).
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Reviews From Top Reviewers

View all reviews
(1-5 of 5)

Apache Airflow

Rating: 8 out of 10
Incentivized
April 04, 2022
PM
PRABHAT MISHRA
Senior Devops Engineer
LEAD School (Education Management, 1001-5000 employees)
Vetted Review
Verified User
Apache Airflow
1 year of experience
View profile
Use Cases and Deployment Scope
We are using apache airflow for managing the ETL pipelines. We are using programmatically to monitor the data pipeline. I have been helping the data team in creating the pipeline using apache airflow.
Pros
  • We are using for the workflow management system
  • managing the etl pipelines.
  • We can manage the task scheduling as code & need not monitor as there is no data in & out.
Cons
  • they should bring in some time based scheduling too not only event based
  • they do not store the metadata due to which we are not able to analyze the workflows
  • they only support python as of now for scripted pipeline writing
Likelihood to Recommend
We were using it for managing the workflows for the etl pipelines as code so Airflow came as very helpful.
Apache Airflow Feature Ratings
Workload Automation (6)
83.33333333333334%
8.3
Multi-platform scheduling
80%
8.0
Central monitoring
70%
7.0
Logging
80%
8.0
Alerts and notifications
90%
9.0
Analysis and visualization
90%
9.0
Application integration
90%
9.0
Return on Investment
  • We had a better understanding of data as ETL pipelines were giving data using airflow
  • We were able to automate most of the ETL pipelines so it reduced manual efforts
  • Airflow UI was extremely helpful which made it easy to understand
Alternatives Considered
Airflow was best suited in my use case for designing the ETL pipelines in a scripted manner for workflows & the UI was very good & easy to use.
Other Software Used

Scalable Scheduling Framework and Orchestration tool

Rating: 9 out of 10
Incentivized
May 30, 2025
AV
Anshuman Varshney
Engineering Manager
Gameskraft (Information Technology & Services, 501-1000 employees)
Vetted Review
Verified User
Apache Airflow
6 years of experience
Use Cases and Deployment Scope
We are using Apache Airflow as an orchestration tool in data engineering workflows in gaming product.
We are scheduling multiple jobs i.e hourly / daily / weekly / monthly.
We have a lot of requirement for dependent jobs i.e job1 should mandatory run before job2, and Apache Airflow does this work very swiftly, we are utilising multiple Apache Airflow integration with webhook and APIs. Additionally, we are doing a lot of jobs monitoring and SLA misses via Apache Airflow features
Pros
  • Job scheduling
  • Dependent job workflows
  • Failure handling and rerun of workflows
Cons
  • Better User Interface
Likelihood to Recommend
Dependent Job scheduling
Rerun mechanism of workflows
High availability deployment strategies
Apache Airflow Feature Ratings
Workload Automation (6)
98.33333333333334%
9.8
Multi-platform scheduling
100%
10.0
Central monitoring
100%
10.0
Logging
100%
10.0
Alerts and notifications
100%
10.0
Analysis and visualization
100%
10.0
Application integration
90%
9.0
Return on Investment
  • Good in job scheduling and dependency management between jobs
  • Robust framework to monitor jobs and alert in case of failure and SLA misses
  • Great integration with multiple open source tools
Usability
10
Easy to learn
Easy to use
Robust workflow orchestration framework
Good in dependent job management
Alternatives Considered
Open source
Easy to configure
Easy to learn
Robust and reliable
Other Software Used

One Stop solution for all the Orchestration needs.

Rating: 10 out of 10
Incentivized
July 07, 2025
RS
Raghwendra Singh
SDE 4
Meesho (Information Technology & Services, 1001-5000 employees)
Vetted Review
Verified User
Apache Airflow
6 years of experience
Use Cases and Deployment Scope
I am part of the data platform team, where we are responsible for building the platform for data ingestion, an aggregation system, and the compute engines. Apache Airflow is one of the core systems responsible for orchestrating pipelines and scheduled workflows. We have multiple deployments of Apache Airflow running for different use cases, each with a workflow of 5,000 to 9,000 DAGs and executing even more DAGs. The Apache Airflow now also offers HA with scheduler replicas, which is a lifesaver and is well-maintained by the community.
Pros
  • Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
  • Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
  • Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.
Cons
  • To achieve a production-ready deployment of Apache Airflow, you require some level of expertise. A repository of officially maintained sample configurations of Helm charts will be handy for a new team.
  • As airflow is used to build many data pipelines, a feature for building lineage using queries for different compute engines will help develop the data catalog. Typically, multiple tools are required for this use case.
  • For building a data pipeline from upstream to downstream tables, using Airflow with lineage to trigger the downstream DAGs after recovery will be helpful. Additionally, creating a dependency between the DAGs would be beneficial.
Likelihood to Recommend
Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
Apache Airflow Feature Ratings
Workload Automation (6)
86.66666666666666%
8.7
Multi-platform scheduling
100%
10.0
Central monitoring
90%
9.0
Logging
80%
8.0
Alerts and notifications
80%
8.0
Analysis and visualization
80%
8.0
Application integration
90%
9.0
Return on Investment
  • By using Apache Airflow, we were able to build the data platform and migrate our workloads out of Hevo Data.
  • Airflow currently powers the datasets for the entire company, supporting analytics backends, data science, and data engineering use cases.
  • We can scale the DAGS from < 1000 to currently> 8000 dag runs per day using HA and worker scaling.
Usability
8
In terms of deploying Airflow, you do need some level of technical expertise to set up the deployment, such as knowledge about Kubernetes if you are setting up the Apache Airflow cluster on Kubernetes. Writing DAGs is straightforward, as examples are available for different operators, and the Apache Airflow documentation is also well-maintained.
Alternatives Considered
Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.
Other Software Used

Apache Airflow for Automation and scheduling

Rating: 9 out of 10
Incentivized
May 05, 2022
Verified User
Program Manager in Manufacturing
Automotive Company, 5001-10,000 employees
Vetted Review
Verified User
Apache Airflow
3 years of experience
Use Cases and Deployment Scope
We are using Apache Airflow for streamline the data pipelines, creating the workflow, Schedule the workflow as per the need, and also monitor the same, we are solving the problem of fetching the data from hive and then created the complete workflow and also we are using for automation as well.
Pros
  • Smart Automation
  • Highly Scalable
  • Complex Workflow
  • Easy Integration with other system
Cons
  • Documentation part
  • GUI can be improved
  • Reliability issues
Likelihood to Recommend
Apache Airflow is best suited for the data engineers for creating the data workflows, and it is best suitable for the scheduling the workflow and also we can run the python codes as well using apache airflow, and it is suited for the situation where we need scalable solution. Monitoring can be done easily.
Apache Airflow Feature Ratings
Workload Automation (6)
85%
8.5
Multi-platform scheduling
80%
8.0
Central monitoring
90%
9.0
Logging
80%
8.0
Alerts and notifications
80%
8.0
Analysis and visualization
90%
9.0
Application integration
90%
9.0
Return on Investment
  • Overall positive impact using apache airflow
  • Easy to use
  • Easy to integrate which saves time
Alternatives Considered
Overall using Apache Airflow is easy to use compare than other other tools available in the market, It is easy to integrate in apache airflow and the workflow can be monitored and scheduling can be done easily using apache airflow, recommend this tool for Automating the data pipeline or workflow.
Other Software Used

Apache AirFlow - Love the Features, Love the Reliability.. Love if the UI get modenized!

Rating: 7 out of 10
Incentivized
June 26, 2022
VT
Victor Tay
Engineer
IronNet CyberSecurity (Computer Software, 201-500 employees)
Vetted Review
Verified User
Apache Airflow
1 year of experience
Use Cases and Deployment Scope
We use apache airflow as part of our DAG scheduler and health monitoring tool. It serves as a core component in ensuring our scheduled jobs are run, the ability to allow us to inspect jobs successes and failures, and as a troubleshooting tool in an event of job errors/failures. It has been a core tool and we are happy with what it does.
Pros
  • Job scheduling - Pretty straightforward in terms of UI.
  • Job monitoring - Dashboard is as straightforward as it gets.
  • Troubleshooting jobs - ability to dive into detailed errors and navigate the job workflow.
Cons
  • UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
  • Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
  • Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
Likelihood to Recommend
For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.
Apache Airflow Feature Ratings
Workload Automation (6)
88.33333333333334%
8.8
Multi-platform scheduling
100%
10.0
Central monitoring
100%
10.0
Logging
100%
10.0
Alerts and notifications
80%
8.0
Analysis and visualization
70%
7.0
Application integration
80%
8.0
Return on Investment
  • It is a good workflow job scheduler.
  • It meets all, if not most of our organization product requirements.
  • AirFlow stability in terms of the product reliability is unmatched.
Alternatives Considered
Using Jenkins and Kafka, it is not for the same purpose, although it might be similar. I would say AirFlow is really what it says on the can - workflow management. For our organisation, the purpose is clear. So long your aim is to have a rich workflow scheduler and job management, AirFlow is the go-to. Use the tool for what it's meant for, and it will meet your need for sure.
Other Software Used
Jenkins, Apache Kafka, Redis™*
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