--- title: Apache Airflow Reviews meta_title: 'Apache Airflow Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 128 reviews by the users' company size, role or industry to find out how Apache Airflow works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 128 scale: '5' date_modified: '2026-07-12' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # Apache Airflow Reviews **Vendor:** The Apache Software Foundation **Category:** [AI Orchestration Software](https://www.g2.com/categories/ai-orchestration) **Average Rating:** 4.4/5.0 **Total Reviews:** 128 ## About Apache Airflow Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs. Key Features: - Pure Python: Workflows are defined using standard Python code, allowing for dynamic pipeline generation and easy integration with existing Python libraries. - User-Friendly Web Interface: A robust web application enables users to monitor, schedule, and manage workflows without the need for command-line interfaces. - Extensibility: Users can define custom operators and extend libraries to fit their specific environment, enhancing the platform's flexibility. - Scalability: Airflow's modular architecture and use of message queues allow it to orchestrate an arbitrary number of workers, making it ready to scale as needed. - Robust Integrations: The platform offers numerous plug-and-play operators for executing tasks across various cloud platforms and third-party services, facilitating easy integration with existing infrastructure. Primary Value and Problem Solving: Apache Airflow addresses the challenges of managing complex data workflows by providing a scalable and dynamic platform for workflow orchestration. By defining workflows as code, it ensures reproducibility, version control, and collaboration among teams. The platform's extensibility and robust integrations allow organizations to adapt it to their specific needs, reducing operational overhead and improving efficiency in data processing tasks. Its user-friendly interface and monitoring capabilities enhance transparency and control over workflows, leading to improved data quality and reliability. ## Apache Airflow Pros & Cons **What users like:** - Users appreciate the **ease of use** of Apache Airflow, facilitating workflow design and monitoring with intuitive features. (35 reviews) - Users appreciate the **intuitive web UI** of Apache Airflow, enabling efficient monitoring and debugging of workflows. (18 reviews) - Users value the **flexibility** of Apache Airflow, allowing customized workflows and seamless integration with various services. (13 reviews) - Users appreciate the **automation capabilities** of Apache Airflow, praising its simplicity and effectiveness in scheduling tasks. (10 reviews) - Users highlight the **easy integrations** of Apache Airflow, enhancing flexibility for diverse workflows and data sources. (10 reviews) - Users appreciate the **extensive integrations** of Apache Airflow, enabling seamless connections with various applications and data sources. (10 reviews) - Users love the **intuitive Python interface** of Apache Airflow, making it easy to set up and manage workflows. (9 reviews) - Efficiency (6 reviews) - Scalability (6 reviews) - Development Ease (4 reviews) **What users dislike:** - Users face a **difficult setup** with Apache Airflow, learning nuances that can complicate initial configuration and usage. (13 reviews) - Users find the **learning curve challenging** , requiring significant time to master operators and the scheduling system. (9 reviews) - Users find Apache Airflow has a **steep learning curve** , making initial setup and configuration quite challenging for newcomers. (8 reviews) - Users find Apache Airflow has a **steep learning curve** that complicates job setup and debugging, making it challenging. (6 reviews) - Users find the **outdated user interface** of Apache Airflow contributes to a less efficient and seamless experience. (6 reviews) - Users find the **UI clumsy and daunting** , impacting usability and the overall experience with Apache Airflow. (6 reviews) - Complexity (5 reviews) - Users find the **interface complexity** of Apache Airflow challenging, requiring significant technical knowledge for effective use. (5 reviews) - Missing Features (5 reviews) - Performance Issues (5 reviews) ## Apache Airflow Reviews ### 1. Best scheduling platform, easy to code in python. **Rating:** 5.0/5.0 stars **Reviewed by:** Ashita K. | Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** October 03, 2023 **What do you like best about Apache Airflow?** Use of cron tab expression, importing various modules, importing user defined operators. **What do you dislike about Apache Airflow?** declaring dag in a fixed pattern or else the scheduler won't pick up ypur dag and show import error **What problems is Apache Airflow solving and how is that benefiting you?** we are using appache airflow to pick up data from various sources such as s3 , adls and dumping the data in some other sink of our choice. ### 2. Powerful Tool **Rating:** 5.0/5.0 stars **Reviewed by:** Ishant T. | Mid-Market (51-1000 emp.) **Reviewed Date:** July 18, 2023 **What do you like best about Apache Airflow?** Airflow is very scalable Dynamic Pipeline integration We can easily define our own operator by extending pre defined libraries We can connect Airflow with so many applications and Data Warehouses like Databricks, MySQL and so on **What do you dislike about Apache Airflow?** User Interface Struggles It is sometime hectic to manage the metadata database of Airflow Performance Struggles sometimes when we create numerous tasks Limited built in features **What problems is Apache Airflow solving and how is that benefiting you?** We use Apache Airflow to design our pipelines and run the Airflow job using the Databricks cluster. And it is very easy to manage the pipeline and integrate Databricks with Airflow using built-in connector ### 3. Best for someone starting on data warehouse **Rating:** 4.0/5.0 stars **Reviewed by:** Vinol D. | Head of Data, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2023 **What do you like best about Apache Airflow?** - Best open source software to get started on - Great material online to troubleshoot and community **What do you dislike about Apache Airflow?** - Needs a dedicated data engineer and devops - Maintianence could take lot of time - needs another tool for data quality measurement **What problems is Apache Airflow solving and how is that benefiting you?** - Solving centraling our data pipelines and business logics - Orchestration and automation of ETLs ### 4. Airflow: Needs a refresher for the modern data stack **Rating:** 4.0/5.0 stars **Reviewed by:** Anup J. | Enterprise (> 1000 emp.) **Reviewed Date:** March 30, 2023 **What do you like best about Apache Airflow?** The best thing about Airflow is how versatile of a tool it is. Airflow can be used to build workflow on just about every database and tool and the sheer wealth of integrations it has is brilliant and just all-round useful **What do you dislike about Apache Airflow?** Learning Airflow is a seriously complicated task. And even that is not often enough to become truly good at it. The scheduling system is hardly intuitive. Versioning for the Dags and reverting them a very simple task in a competitor Prefect is not a part of Airflow at all **What problems is Apache Airflow solving and how is that benefiting you?** Batch orchestration and scheduling is the fundamental problem that Airflow solves. We use the Airflow operators to build DAGs(Directed Acyclic Graphs) which have parts of our processes, including preprocessing, training and monitoring. ### 5. I have used Airflow for developing pipeline in my recent company **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Small-Business (50 or fewer emp.) **Reviewed Date:** June 22, 2023 **What do you like best about Apache Airflow?** Python is my favorite coding language, the DAGs in Airflow are written in Python. There are several built-in operators in Airflow to execute the Python function, call Databricks job and execute bash commands. I love to build the pipeline in Airflow **What do you dislike about Apache Airflow?** The Airflow orchestration tool is a bit complicated in developing compared to other pipeline tools. While other tools have drag-and-drop options, coding in Airflow takes more time. **What problems is Apache Airflow solving and how is that benefiting you?** We are using it to build the pipeline to extract data from multiple sources dynamically by generating the parameters using a config file. Adding the new source would require minimal code changes. ### 6. Easy and good product **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** August 17, 2023 **What do you like best about Apache Airflow?** It is easy configure it is easy to handle script over UI It shows error on UI where your script got error Can run script easily **What do you dislike about Apache Airflow?** For multiple file triggering it is a bit difficult **What problems is Apache Airflow solving and how is that benefiting you?** Easy to access scripts over servers Finding out error on UI rather than searching in server logs. Can run script easily over server using airflow UI ### 7. My experience with Airflow **Rating:** 4.5/5.0 stars **Reviewed by:** Deril R. | Student, Enterprise (> 1000 emp.) **Reviewed Date:** December 20, 2022 **What do you like best about Apache Airflow?** - easy scheduling - python framework, so easy to learn - can be dockerized easily with some tutorials - easy to learn even for beginners. - better than other scheduling tools **What do you dislike about Apache Airflow?** - the UI can be made more customizable. - concurrency is low if you have a small system. - the UI can be daunting for a few people like managerial positions. - security can be improved **What problems is Apache Airflow solving and how is that benefiting you?** - easy scheduling for people with technical knowledge. - since it is in python, it grants more flexibility than competitors like job scheduler. - good skill to learn for different data engineering roles ### 8. Great Functionality. Unsure if it matches my use case **Rating:** 4.0/5.0 stars **Reviewed by:** Nathan P. | Machine Learning Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** December 20, 2022 **What do you like best about Apache Airflow?** I really like the ability to view the failure and status of each step in a workflow quickly. I like that it has the ability to retry only what fails and gives a lot of control. **What do you dislike about Apache Airflow?** I do not like that there is a good deal of latency between starting tasks with the default settings. I might be able to reduce it but it will require a decent effort to do so. **What problems is Apache Airflow solving and how is that benefiting you?** It allows me to create workflows of data tasks in a way that allows me to monitor the status of all the workflows. I would like to use it for a backend of an ML application. ### 9. Awesome pipelining tool -- Apache Airflow **Rating:** 4.0/5.0 stars **Reviewed by:** Naman G. | Teaching Assistant, Mid-Market (51-1000 emp.) **Reviewed Date:** October 12, 2022 **What do you like best about Apache Airflow?** Apache Airflow provides a very good user interface and it is very easy to work on this tool. It also provides various features for the representation of pipelines. It provides several stages for the DAG like running, failed, etc. There are different colors for different stages in this tool. **What do you dislike about Apache Airflow?** When we run any DAG in the Apache Airflow the DAG failed when it will not get the desired file from upstream but it does not make proper logs of the successful stage. We need to check the logs in the EC2 logs whether our data is successfully loaded or not. Most of the time Apache Airflow shows success on the DAG but actually, the job failed. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow provides an awesome UI and shows pipelines in a graphical manner which is very easy to understand and work. I have used Apache Airflow to make the ETL pipelines using python scripts and this provided me with a very awesome graphical view of my scripts. ### 10. Airflow is prolific for Data Engineering **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Enterprise (> 1000 emp.) **Reviewed Date:** May 12, 2023 **What do you like best about Apache Airflow?** It is something many use and feel comfortable with. That huge ecosystem provides alot of benefits. **What do you dislike about Apache Airflow?** It is fundamentally a flawed design. They are making progress in overcoming some of the scaling issues, and it is improving **What problems is Apache Airflow solving and how is that benefiting you?** Airflow is what we use for many of our data orchestration tasks. When we want to have multi-step pipelines to accomplish moving things around and/or kicking off various other forms of compute, we reach for Airflow. ### 11. powerful tool, a must have for any data team **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consumer Goods | Mid-Market (51-1000 emp.) **Reviewed Date:** January 30, 2023 **What do you like best about Apache Airflow?** The pre-defined operators make working with Airflow very easy. Instead of re-inventing the wheel, the community provides tools that can be shared and re-used for faster development. **What do you dislike about Apache Airflow?** Airflow scripts cannot be tested outside of the Airflow environment. You cannot run individual lines of code, which makes it harder for stepping through the script. **What problems is Apache Airflow solving and how is that benefiting you?** Customized validations and alerting allow for trustworthy data pipelines. The ad-hoc triggers allow for unscheduled job runs as needed. The integration with Slack makes it a convenient tool. ### 12. Best workflow orchestration tool ever **Rating:** 5.0/5.0 stars **Reviewed by:** Sagar C. | Senior Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** I like the airflow features such as dags/scripts monitoring. We can even track past 365 days history and logs. TriggerDagrun operator is best for dependency management across all dags. **What do you dislike about Apache Airflow?** I am missing connections feature across multiple sources in apache Airflow which can help us to manage end to end ETL system. Currentely it is taking sometime to Start dag/pod, it would be great if they can make it quick. **What problems is Apache Airflow solving and how is that benefiting you?** Apache airflow solving complex workflow management in a easy way. It's benefitting me to track all the scheduled script at one place with parallel processing and i can easily make customization in kubernates operator parameters as per the requirements. ### 13. An one man army **Rating:** 5.0/5.0 stars **Reviewed by:** Jayanna T. | Senior Consultant, Enterprise (> 1000 emp.) **Reviewed Date:** December 23, 2022 **What do you like best about Apache Airflow?** Easy to use the tool. Contains all kinds of processors which support all requirements. Support isolation of projects using processor group. Also helps to send or receive data from the remote cluster **What do you dislike about Apache Airflow?** If we have less memory in the cluster, it does not support auto-scaling. If one processor fails, we need to restart everything from scratch. Sometimes we have seen the flow file corruption **What problems is Apache Airflow solving and how is that benefiting you?** Data ingestion from multiple sources like remote machines using getSFTP processor. Capture the CDC data from RDBMS. Streaming data from kafka to HDFS . pushing data to remote nifi cluster ### 14. Dynamic and scalable pure python product **Rating:** 4.5/5.0 stars **Reviewed by:** Kumar S. | Associate AI/ML Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 08, 2022 **What do you like best about Apache Airflow?** It is an open source platform for scheduling, monitoring and developing data and compute pipelines very easily. It uses python to create workflows and it can run anything in the world. It is easy to use. Open source community is good. Good set of ready to use operators. Easy coding with python. And good graphical UI **What do you dislike about Apache Airflow?** Although it is really good platform for managing workflows still it has some issues like there is no true way to monitor the quality of your data. Learning airflow requires a time investment. No guide on best practices on developing DAGs. No versioning in the airflow scheduler. Debugging is very time consuming. **What problems is Apache Airflow solving and how is that benefiting you?** Apache airflow is solving many problems related to modern data engineering and computing. It is really good workflow management platform for ETL and Data science. It has good integration with the cloud services and cloud providers like Google, aws and azure. ### 15. Airflow has been an essential part of my Data Engineering Life **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Retail | Enterprise (> 1000 emp.) **Reviewed Date:** September 09, 2022 **What do you like best about Apache Airflow?** With a gradual learning curve, Airflow helped schedule our workflows and trigger airflow DAGs from other GCP products like cloud function, allowing for event-based workflows dependent on multiple GCP products and data sources from various teams. **What do you dislike about Apache Airflow?** Apache airflow is just an orchestrator, and it's unreliable with jobs with higher run-time, like ML model training using Vertex Custom Job or some BigQuery SQL scripts that run for a long time. We have also faced situations where the BQ job ran successfully, but Airflow had lost connection. There are also connection issues with dataproc pyspark jobs. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow is solving the problem of orchestrating workflows, including extraction, transformation and loading of data from source to destination. In addition, it has helped us data engineers support data scientists with the data in the required format and location. ### 16. What you should know about airflow **Rating:** 4.5/5.0 stars **Reviewed by:** Adekunle M. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 06, 2023 **What do you like best about Apache Airflow?** It is a very great tool for job orchestration in the data processing pipelines. It helps me sleep well at night as i can scheduled my job prior before the time needed. **What do you dislike about Apache Airflow?** It requires a little complexity and technical background for optimal use . someone without the knowledge of programming cannot be confidently use this tool **What problems is Apache Airflow solving and how is that benefiting you?** Job Orchestration Task Scheduling ### 17. Futuristic, Best Scheduler | Worth Every Penny **Rating:** 4.5/5.0 stars **Reviewed by:** Quazi A. | Data Engineer 2, Enterprise (> 1000 emp.) **Reviewed Date:** September 08, 2022 **What do you like best about Apache Airflow?** The ease of managing Data pipelines , ML workflows has helped developers focus on other aspects. The DAGS are pretty straightforward to implement and efficient The Scheudler is far more ahead when it comes to peers **What do you dislike about Apache Airflow?** The Metadata DB management on a long term perspective seems to be tedious for Airflow developer The Airflow starts to show slowness in dag updates of the metadata sb is populated to extent In Airflow 2 , there is no Option for ad hoc query , which is kinda trouble when we need check metadata DB **What problems is Apache Airflow solving and how is that benefiting you?** The way airflow manages E2E pipeline has helped me as developer to built super scalable and dynamic data ingestion pipelines which would help organisations in future to not worry on scalibility and performance ### 18. it is an excellent tool to manage jobs and Dags **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 21, 2023 **What do you like best about Apache Airflow?** flexibility and ease in writing dags. multiple operator and executors to run the job. **What do you dislike about Apache Airflow?** somtime shceduler with too many jobs takes too much CPU of DB. so it need to have bigger db most of the time for high scale. **What problems is Apache Airflow solving and how is that benefiting you?** managing jobs and DAG while manitaining flexibility to run them, ### 19. Airlow user review **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** Airflow is way better orchestration tool than any other tool in the market.it is based on python so it is super easy to create dags and schedule it.there are plenty of inbuilt operators which performs Swift operation.creating cluster and replying dags in GCP is super easy **What do you dislike about Apache Airflow?** But I still feels there are some capabilities still needs to be built like dataflow. Plus installation in your local system should be more easy and documentation should be there . Also dependency between dags should also be improved. **What problems is Apache Airflow solving and how is that benefiting you?** I was building a datapipline to move data from bigquery to GCS bucket. I researched some options but Airflow turns out to be the best . Set up is very easy in GCP . ALL you need to code in notepad and upload it in dag folder as python file. Volia! you can see your job in airflow. . And it is easy to code in python ### 20. Very effective for data **Rating:** 5.0/5.0 stars **Reviewed by:** Iván R. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** February 02, 2023 **What do you like best about Apache Airflow?** The simplicity of use and how it facilitates its use for teams. **What do you dislike about Apache Airflow?** The lack of community and use cases or best practices. **What problems is Apache Airflow solving and how is that benefiting you?** We have replaced Jenkins as the deployment tool for the creation and updating of the main pipelines, without the need for sysadmins. ### 21. Best tool to automate multiple workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Parag P. | Lead Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** Airflow provides all the features like DAG like workflows and action and scripts running features. It also integrate with running environments like celery or kubernetes that helps to run multiple job parallel. **What do you dislike about Apache Airflow?** It takes times to setup environment and for enty lavel personal it is difficult to setup it. I think if airflow provides cloud tool integration and easy setup it will help many users **What problems is Apache Airflow solving and how is that benefiting you?** It helps me to automate Ai pipeline those ware manuals It helps to me save lot of time as well as I can see logs and get alerts if anything goes wrong in my pipeline ### 22. Apache Airflow is an amazing tool to run and monitor ETL Jobs with intuitive UI. **Rating:** 4.5/5.0 stars **Reviewed by:** Rohit A. | Senior Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** September 17, 2022 **What do you like best about Apache Airflow?** Apache Airflow handles dependency management well, an advantage over typical ETL monitoring tools. The User Interface of Airflow is excellent and offers a lot of tasks, such as monitoring runs and logging quickly. **What do you dislike about Apache Airflow?** Apache Airflow is not directly compatible with Windows operating system. There are ways to overcome this limitation, but it can be challenging for new data engineers. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow is helping us run and monitor complex data pipelines and set up complicated tasks with automated design so that it requires minimum human interrvention. ### 23. Handy tool but can be improved. **Rating:** 4.0/5.0 stars **Reviewed by:** Yash G. | Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** September 21, 2022 **What do you like best about Apache Airflow?** A handy tool for creating a fast and quick DAG utility for a GCP. Works well to parallelize spark jobs. **What do you dislike about Apache Airflow?** It would be nicer if the DAG GUI could be improved with functionality to add logs from the tasks. Also the error handling and restart from the point of failure like azure adf. **What problems is Apache Airflow solving and how is that benefiting you?** Improve the GUI: Help a newbie learn and adapt faster without much hand-holding/guidance. Make it interactive and add drag and drop utility with it. ### 24. Robust Orchestrator and scheduling tool for building data pipelines **Rating:** 4.0/5.0 stars **Reviewed by:** BELLUM M. | Back End Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 13, 2022 **What do you like best about Apache Airflow?** DAGs allow to run of scripts in the logical task flow. It ability to show logs and emails **What do you dislike about Apache Airflow?** Little bit of issue in setting up for production environment **What problems is Apache Airflow solving and how is that benefiting you?** It is solving scheduling to trigger processes and also able to create a workflow ### 25. Apache Airflow is very intersting and useful tool for data engineers to manage thire data pipelines. **Rating:** 5.0/5.0 stars **Reviewed by:** Ankesh P. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** Using Apache airflow reduces so many manual efforts which are required in project data pipelines, and it is best tool since its come with so many features such as custom operators and many more **What do you dislike about Apache Airflow?** Airflow should work on dag versioning so that we can manage its versions more effectively **What problems is Apache Airflow solving and how is that benefiting you?** A problem such as the cost reduction of maintaining data pipelines and managing data pipelines was initially challenging. After airflow, its becomes very easy ### 26. Neutral but even more than neutral & a bit less than best **Rating:** 3.5/5.0 stars **Reviewed by:** Shreshth S. | Data Science Intern, Small-Business (50 or fewer emp.) **Reviewed Date:** September 14, 2022 **What do you like best about Apache Airflow?** It's relatively smooth comparatively with ease of using the features which impressed due to the reason that others aren't providing them **What do you dislike about Apache Airflow?** The optimisation is lacking along with processing speed compared to other similar platforms. **What problems is Apache Airflow solving and how is that benefiting you?** Helps and helps to work on data pipelines over object stores and data warehousing as well; hence the working style gets more productive. ### 27. Let the airflow handle the job **Rating:** 4.5/5.0 stars **Reviewed by:** Aditya V. | Data Scientist II, Enterprise (> 1000 emp.) **Reviewed Date:** September 11, 2022 **What do you like best about Apache Airflow?** Handles dependency management like a charm. From dataflow to conplex branching, task retry, catchup runs etc., It has everything **What do you dislike about Apache Airflow?** Data processing is not very intuitive even after using it for a while and tedious to understand for someone who is new to it **What problems is Apache Airflow solving and how is that benefiting you?** Effectively handles the complex process of data flow through the pipelines ### 28. One of the best orchestrator (especially Airflow2) **Rating:** 4.0/5.0 stars **Reviewed by:** Viorel Cristian D. | ETL Developer, Enterprise (> 1000 emp.) **Reviewed Date:** June 29, 2022 **What do you like best about Apache Airflow?** The UI with multiple views and the option to run each job without going into debug mode. **What do you dislike about Apache Airflow?** There is no option to change the code in the UI. **Recommendations to others considering Apache Airflow:** great orchestrator that is pretty easy to use once you get used to it. **What problems is Apache Airflow solving and how is that benefiting you?** Orchestrating Talend jobs, ADF pipelines, Databricks notebooks and Snowflake scripts. ### 29. Automation with Airflow **Rating:** 4.5/5.0 stars **Reviewed by:** Jay C. | Associate Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** Web UI and transparency, It has been the most preferred tool to automate Jobs or Workflows **What do you dislike about Apache Airflow?** After run of every job we may face some errors and to check the logs we don't have keyword search filter. **What problems is Apache Airflow solving and how is that benefiting you?** Automation with Airflow has itself solved up my all problems ### 30. Create a job and let the airflow the work. **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Internet | Mid-Market (51-1000 emp.) **Reviewed Date:** July 17, 2022 **What do you like best about Apache Airflow?** It is an open-source tool that provides users to schedule a task and automate the process. It provides the option to run jobs individually without using debug mode. It is easy to use and fun. Moreover, it is easy to understand. **What do you dislike about Apache Airflow?** UI glitches sometimes. Apart from that, modifying a deployed pipeline is complex. Therefore, usually, users need to create a new pipeline instead of making changes to the existing one. **What problems is Apache Airflow solving and how is that benefiting you?** Once for a project, I used Airflow to automate the report-sharing process with the stakeholders. I used to extract the data and mail it to stakeholders daily. However, now I have scheduled a task using Airflow to mail my reports to stakeholders. ### 31. Best ochestrator in the market **Rating:** 5.0/5.0 stars **Reviewed by:** Yash M. | Senior Software Engineer / Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 16, 2022 **What do you like best about Apache Airflow?** Ease of creating dags, communication b/w tasks, retries, multiple connectors etc **What do you dislike about Apache Airflow?** Inhouse setup fo airflow can be tedeious, also not knowing why tasks are in scheduled state **What problems is Apache Airflow solving and how is that benefiting you?** It helps us make complicated data pipelines which are internally dependent on various databases and spark emr ### 32. Scheduling just like the name airflow **Rating:** 3.5/5.0 stars **Reviewed by:** Sushant D. | Business Intelligence Engineer - 2, Small-Business (50 or fewer emp.) **Reviewed Date:** September 06, 2022 **What do you like best about Apache Airflow?** Orchestration of the jobs is very easy and done with ease along with many options for customisation. **What do you dislike about Apache Airflow?** Nothing as such, debugging could be improved to track down the failures. **What problems is Apache Airflow solving and how is that benefiting you?** Orchestration of complex data pipelines and integration with multi cloud architecture. ### 33. Best tool for workflow management with very strong community. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Market Research | Small-Business (50 or fewer emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** 1. User friendly 2. Ease of deployment 3. Very strong and active community 4. Opensource 5. Plug and play 6. Small learning curve if you know Python **What do you dislike about Apache Airflow?** 1. Zombie process on Kubernetes are hard to stop **What problems is Apache Airflow solving and how is that benefiting you?** 1. We replaced celery with apache airflow for task management resulted into better performance 2. Using it to mange ETL pipeline ### 34. Very powerful tool **Rating:** 4.5/5.0 stars **Reviewed by:** Lingjing S. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** November 29, 2021 **What do you like best about Apache Airflow?** Airflow is a very widely accepted workflow scheduling tool in the industry. It has excellent documentation and support. It is very powerful and flexible (for example, it can be integrated with tableau API even to schedule tableau data source refresh once the pipeline has finished). **What do you dislike about Apache Airflow?** One of the greatest challenge is that the learning curve will be a little bit deep, and some of the functions, like when the pipeline will start kicking off can be confusing. Besides, the backfill has to be done mannually. And once you have deployed the pipeline, it is difficult to make changes. Usually you will need to build a new pipeline instead of modifying the old one. **Recommendations to others considering Apache Airflow:** As a data analyst who works on creating the pipeline to power dashboards, I would suggest you to go through the documentation of airflow before get your hands dirty. How airflow will behave is sometimes different than expected and reading the documentation is the best way to close those gaps. **What problems is Apache Airflow solving and how is that benefiting you?** We use airflow to run pipelines (building dependency of the tables and schedule the pipelines to run at a specific time). You can use the tool to visualize the dependency to help you understand the workflow as well. Personally as an analyst, I used airflow a lot of build pipelines to customize the workflow (sometimes I need it to integrate with Tableau). ### 35. Need more awareness about the product **Rating:** 2.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** September 08, 2022 **What do you like best about Apache Airflow?** A product which helps me in deploying without much mlops knowledge, easy to use **What do you dislike about Apache Airflow?** Need documentation, writeups, blogs and awareness about the product **What problems is Apache Airflow solving and how is that benefiting you?** Mostly to solve my project deployment, I need not create the entire modularization and mlops pipeline so, it's useful in that way ### 36. The best scheduler out there **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** June 21, 2022 **What do you like best about Apache Airflow?** Open source, you can find operators for almost any tool or service you want to use with it. It is so easy to use and you need just a basic understanding of Python to work with it. **What do you dislike about Apache Airflow?** sometimes airflow lags out when updating the status of running or failed tasks. UI glitches out sometimes. **Recommendations to others considering Apache Airflow:** Go for it, its the best scheduler out there. **What problems is Apache Airflow solving and how is that benefiting you?** I am using airflow to schedule and monitor data pipelines. It works great so far without minimum issues. ### 37. It’s very nice DAG based job sheduling tool. I will recommend everyone to use at least once. **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Banking | Small-Business (50 or fewer emp.) **Reviewed Date:** September 08, 2022 **What do you like best about Apache Airflow?** It's very light weight and easy to use tool **What do you dislike about Apache Airflow?** Nothing to dislike at the moment we have not dislike anything yet **What problems is Apache Airflow solving and how is that benefiting you?** It's sheduling the job and there is multiple stages based on graph so it helps to understand the flow of job ### 38. Apache airrlfow is Awesome **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** September 06, 2022 **What do you like best about Apache Airflow?** The best thing about the Airflow is how simple it is to use and best UI experience **What do you dislike about Apache Airflow?** As of now I have not felt any bad experiences about the apache airflow **What problems is Apache Airflow solving and how is that benefiting you?** It is solving most of my bigdata and analytics related issues ### 39. Great experience working with airflow. Easy to use and great GUI. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** September 08, 2022 **What do you like best about Apache Airflow?** Creation of DAGS are really easy and graphical view of dag gives simple yet pretty picture of the complete flow. **What do you dislike about Apache Airflow?** There should be retry option if a job fails. **What problems is Apache Airflow solving and how is that benefiting you?** We mainly used to schedule our daily batches. ### 40. Best Workflow management tool. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Accounting | Mid-Market (51-1000 emp.) **Reviewed Date:** September 14, 2022 **What do you like best about Apache Airflow?** 1. UI 2. Open source 3. Monitoring workflow **What do you dislike about Apache Airflow?** None as of now . Only the statsD metrics don't provide task level info. **What problems is Apache Airflow solving and how is that benefiting you?** It's helping us to create a centralised workflow management tool. ### 41. Airflow: Orchestration in cloud like Air **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** September 20, 2022 **What do you like best about Apache Airflow?** * Open source * Integration capabilities * Run anywhere * No Cloud Monopoly **What do you dislike about Apache Airflow?** * Containerisation problem * More cloud integration **What problems is Apache Airflow solving and how is that benefiting you?** * Orchestrating my Data Engineering pipelines ### 42. I have some experience in working with apache. It's easy to understand and write scripts. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer & Network Security | Mid-Market (51-1000 emp.) **Reviewed Date:** September 13, 2022 **What do you like best about Apache Airflow?** Apache proxy was most useful for me as I could connect different servers easily. **What do you dislike about Apache Airflow?** Nothing as such I have found out at this moment. **What problems is Apache Airflow solving and how is that benefiting you?** Connecting different servers. ### 43. Apache airflow is an amazing data extraction, processing and scheduling tool!!! **Rating:** 4.5/5.0 stars **Reviewed by:** Giridhar P. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** August 06, 2021 **What do you like best about Apache Airflow?** Apache airflow is a freeware that executes components written in python modules. The written python modules are called DAGs. The DAGs need to configured with appropriate connections for successful execution. The connections and variables are easily configurable. The User Interface aspect of the tool makes the visualization the most attractive aspect of the tool. The triggering of the respective DAGs, status of execution, success and failure notification in green and red colors etc are some of the best aspects of Apache airflow. The configuration of variables in the form of json files are easy and straightforward. The different instances of executions along with the individual DAGs statuses historically enable the users to track the logs successfully. The ability to backtrack the logs for all these instances makes this tool a commendable one. Functions-based categorization of the functionality is flexible. **What do you dislike about Apache Airflow?** Apache airflow contains an intensive documentation that needs to be read and reviewed to ensure that the configuration works per your needs. Knowledge of python as a prerequisite is needed. Familiarity with Json file and format is required to some extent. The tool might create an impression of being a bit complex to many users who don't have any background on Python. The formatting of tasks, hooks etc could be a bit complex. Not much of examples are available on open forums and exploring the solution for an end to end functionality could be a bit challenging for new users. **Recommendations to others considering Apache Airflow:** If you want an ETL tool that requires some programming, this tool could be your first choice. The coding in Airflow is not GUI based and hence may not be conducive to all the ETL engineers. However, it could serve all your basic needs. The ability to connect to heterogeneous databases and perform a comparison across these datasets is a provision that is yet to arrive. **What problems is Apache Airflow solving and how is that benefiting you?** Apache airflow is being used in my organization to perform data integration in AWS S3 region. The tool has enabled me to connect successfully to a relational database, perform data extracts and compile the accumulation of extracts in multiple flat file segments. The DAG components are conducive to build and the dependency of tasks within DAGs are easy to be specified. The end to end ETL functionality was implemented successfully using Apache airflow. The tool provides flexibility to trigger different DAGs at any time during the day and ensures that the logs are traced successfully. The tool can be configured successfully on either AWS environment or docker container. It was configured in both environments successfully within my organization and this flexibility is what makes it the best. ### 44. Good orchestrator tool to deal with dependency.. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Consumer Services | Mid-Market (51-1000 emp.) **Reviewed Date:** September 22, 2022 **What do you like best about Apache Airflow?** Different types of Operators and Trigger rules. **What do you dislike about Apache Airflow?** Nothing as such but Web UI can be improved **What problems is Apache Airflow solving and how is that benefiting you?** Orchestarating pipeline ### 45. Data pipelines never been easier **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Small-Business (50 or fewer emp.) **Reviewed Date:** October 15, 2021 **What do you like best about Apache Airflow?** Once you know the strengths of Ariflow create data pipelines is easier than ever before, the data lineage now is super clear since Airflow has a lot of visual integrations **What do you dislike about Apache Airflow?** UI has lots of bug still, some times the UI will show bad or elements will appear in weird places and it would become impossible to click what you need, It happens often enough that it bothers **Recommendations to others considering Apache Airflow:** Take a good Airflow introduction course, it's easy enough to learn it without it, but there are a ton of good practices that you should know to get the full benefits of Apache Airflow **What problems is Apache Airflow solving and how is that benefiting you?** New data pipelines, previously data pipelines, and time triggers were done with an inhouse tool that gave us a lot of problems, Airflow brings a lot of standardization to the project as well as modularity to the code ### 46. A very handy tool for someone working in ETL & data engineering. **Rating:** 4.5/5.0 stars **Reviewed by:** Nitika K. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** June 26, 2021 **What do you like best about Apache Airflow?** They are a lot many things that I absolutely love about Airflow, some of them are: - Authentication on your webserver. You don't need to set up your own auth system, it comes out of the box with Airflow. - Very user-friendly and information-rich User Interface to trigger your DAGs. - Makes you absolutely free from manually running the most used scripts. **What do you dislike about Apache Airflow?** Nothing as of now. Airflow meets all my requirements. **What problems is Apache Airflow solving and how is that benefiting you?** As a data engineer, I have to regularly run ETL pipelines & data processing scripts. Earlier I used to manually trigger them based on my schedule. Now ith Airflow, I can schedule the scripts to run based on other script conditions. ### 47. Makes orchestrating workflows through DAGs easy! **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Leisure, Travel & Tourism | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2022 **What do you like best about Apache Airflow?** You can orchestrate many DAGs at once at different schedules and can easily go back in time to reproduce/replicate runs. **What do you dislike about Apache Airflow?** The Airflow CLI is not as friendly as the Airflow UI. The documentation is sometimes not clear/misses details. **What problems is Apache Airflow solving and how is that benefiting you?** All our DAGs are built in Airflow, these are critical to our business. ### 48. Airflow is by far the best ETL tool **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Insurance | Mid-Market (51-1000 emp.) **Reviewed Date:** November 24, 2021 **What do you like best about Apache Airflow?** Airflow is easy to get started with and can scale and do more things as your tech stack evolves. Airflow comes with a lot of helpful features out-of-the-box, such as the DAG visualizations and task trees. Furthermore, Airflow is very flexible. You can use it for more than just data transformation. We started running batch ML models in Airflow before we onboarded a third-party tool. **What do you dislike about Apache Airflow?** There is nothing I dislike about Airflow. We started using it less because it requires more engineering support than other tools (that probably run on Airflow), but we will continue to use it because it is the most flexible tool for ETL. We wished that Airflow had a premium tier so that we could get more support. **What problems is Apache Airflow solving and how is that benefiting you?** We use Airflow for the typical data extraction, transformation, and loading. In addition, we use it to send data to partners and score ML models. Airflow has allowed us to implement complex data pipelines easily and at a relatively low cost. ### 49. Makes easy to run complex data pipelines **Rating:** 4.5/5.0 stars **Reviewed by:** Sai Teja K. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** December 14, 2021 **What do you like best about Apache Airflow?** It will execute tasks in order , every task will have required resources. **What do you dislike about Apache Airflow?** There will be no versioning for data pipelines. Lack of data sharing between tasks **What problems is Apache Airflow solving and how is that benefiting you?** Orchestration and scheduling of DAGs ### 50. Not an amazing user experience, but gets the job done **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** November 18, 2021 **What do you like best about Apache Airflow?** The tree view of your ETLs is a great way to visualize how well the DAG is functioning, if it ran when it was supposed to, and the order and dependency of tasks. **What do you dislike about Apache Airflow?** Error messages are difficult to parse, and the navigation isn't intuitive. **What problems is Apache Airflow solving and how is that benefiting you?** Airflow is the tool that triggers our ETLs to run. It's useful as a central location where the whole BI team can reference ETLs in progress, re-run them, or view errors. ## Apache Airflow Discussions - [What is airflow technology?](https://www.g2.com/discussions/what-is-airflow-technology) - 1 comment - [Is airflow a framework?](https://www.g2.com/discussions/is-airflow-a-framework) - 1 comment - [Is Apache airflow an ETL tool?](https://www.g2.com/discussions/is-apache-airflow-an-etl-tool) - 1 comment - [Who is using Apache airflow?](https://www.g2.com/discussions/who-is-using-apache-airflow) - 1 comment - [View Apache Airflow pricing details and edition comparison](https://www.g2.com/products/apache-airflow/reviews?page=2§ion=pricing&secure%5Bexpires_at%5D=2026-07-12+19%3A40%3A05+-0500&secure%5Bsession_id%5D=2a4d74b4-f343-43b7-9f85-2b1730452e2b&secure%5Btoken%5D=c6e09855a1f3af421f9f1eca7f071842cf76881b622a3220290b90fd15fbc9b0&format=llm_user) ## Apache Airflow Integrations - [Amazon EMR](https://www.g2.com/products/amazon-emr/reviews) - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews) - [Amazon Simple Storage Service (S3)](https://www.g2.com/products/amazon-simple-storage-service-s3/reviews) - [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews) - [AWS Bedrock](https://www.g2.com/products/aws-bedrock/reviews) - [AWS CloudFormation](https://www.g2.com/products/aws-aws-cloudformation/reviews) - [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - [AWS Lambda](https://www.g2.com/products/aws-lambda/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews) - [Azure Pipelines](https://www.g2.com/products/azure-pipelines/reviews) - [Erisna](https://www.g2.com/products/erisna/reviews) - [GitHub](https://www.g2.com/products/github/reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Google Cloud Data Fusion](https://www.g2.com/products/google-cloud-data-fusion/reviews) - [Google Cloud Storage](https://www.g2.com/products/google-cloud-storage/reviews) - [Kubernetes](https://www.g2.com/products/kubernetes/reviews) - [Microsoft SharePoint](https://www.g2.com/products/microsoft-sharepoint/reviews) - [OpenVAS](https://www.g2.com/products/openvas/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [Python](https://www.g2.com/products/python/reviews) - [Slack Connector for Jira](https://www.g2.com/products/slack-connector-for-jira/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) - [Tenable Nessus](https://www.g2.com/products/tenable-nessus/reviews) ## Apache Airflow Features **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Workflow Design & Integration - AI Orchestration** - Dependency Management - Workflow Coordination - Multi-Provider API Connectivity - Multi-Step Workflow Creation - Enterprise System Integration - Real-Time Data Pipelines **Management** - Cataloging - Monitoring - Governing - Model Registry **Operations** - Metrics - Infrastructure management - Collaboration **Performance Optimization & Analytics - AI Orchestration** - Workflow Performance Dashboards - Workflow Reporting - Resource Utilization Monitoring - Computational Resource Management - Dynamic Scaling - Component Monitoring **Management** - Cataloging - Monitoring - Governing **Governance & Compliance Controls - AI Orchestration** - Regulatory Compliance - Governance Policy Enforcement - Role-Based Access Control - Audit Trail Management - Security Protocols **Generative AI** - AI Text Generation - AI Text Summarization ## Top Apache Airflow Alternatives - [UiPath Agentic Automation](https://www.g2.com/products/uipath-agentic-automation/reviews) - 4.6/5.0 (6,137 reviews) - [Camunda](https://www.g2.com/products/camunda/reviews) - 4.5/5.0 (317 reviews) - [MuleSoft Anypoint Platform](https://www.g2.com/products/mulesoft-anypoint-platform/reviews) - 4.5/5.0 (654 reviews)