# 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:** 126 ## 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 find Apache Airflow's **ease of use** beneficial for designing and managing complex workflows efficiently. (35 reviews) - Users appreciate the **intuitive web UI** of Apache Airflow, which simplifies monitoring, debugging, and managing workflows. (18 reviews) - Users value the **flexibility** of Apache Airflow, allowing customizable workflows and easy integration for diverse use cases. (13 reviews) - Users value the **automation capabilities** of Apache Airflow, enabling efficient scheduling and orchestration of workflow jobs. (10 reviews) - Users appreciate the **easy integrations** of Apache Airflow, enhancing flexibility and versatility in managing workflows. (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 report that the **difficult setup** of Apache Airflow complicates the initial experience and requires significant expertise. (13 reviews) - Users face a challenging **learning curve** with Apache Airflow, requiring significant time to master its complexities. (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** , making job setup and debugging a challenging experience. (6 reviews) - Users find the **user interface outdated** , impacting usability and responsiveness, especially during heavy DAG operations. (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. Scalable Workflows with Apache Airflow, Best data Engineering tool for Orchestrator,Easy Deployment **Rating:** 4.5/5.0 stars **Reviewed by:** Rajesh K. | Senior Cloud Software Engineer , Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2026 **What do you like best about Apache Airflow?** It is easiy to deploy with docker. Provide secure authentication. A better UI in airlfow3.x. There. is many method, operator, hooks are added. easily to add dependecy. A better workflow monitoring tool. Easily schedule , activate and deactivate jobs. Good performance when used in any cloud platform like aws in serverless mode. easily scaleable. Easy to integrate third part library. Apache airflow is free of cost but you need to pay compute cost if you are using any cloud. A wide support community **What do you dislike about Apache Airflow?** When I create a task in expansion mode, it doesn’t display the graphical flow properly. I also noticed there’s too much logging. And when setting it up on AWS MWAA in a private VPC, it requires additional configuration. **What problems is Apache Airflow solving and how is that benefiting you?** I have thousands of jobs that I need to monitor daily. In the past, we created a custom Python script, but later we found Airflow and started using it. Now, in one place, we can monitor all running jobs along with their status, and we can easily notify the developer or the respective person to check any alerts. It also helps me provide a better orchestration flow. ### 2. Apache Airflow : Flexible and reliable orchestration tool with a learning curve **Rating:** 4.0/5.0 stars **Reviewed by:** Salman K. | Subordinate Consultant, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** March 17, 2026 **What do you like best about Apache Airflow?** What I like most about Airflow is its flexibility and number of features for building workflows using DAGs. It is very useful for managing complex pipelines with dependencies. Integration with different systems is also strong. Once setup is done, it works reliably and is used frequently in day-to-day operations. **What do you dislike about Apache Airflow?** Ease of use is one area where it can improve, especially for new users. Initial setup and implementation take effort if you are managing it yourself. The UI is not very user-friendly and sometimes slow. Also, debugging failed workflows can take time. **What problems is Apache Airflow solving and how is that benefiting you?** Airflow is helping us automate and manage data pipelines in a structured way. Earlier, tasks were manual and not properly scheduled, but now everything runs through defined workflows. It has improved reliability, reduced manual effort, and made it easier to monitor processes. Integration with multiple systems also helps in handling end-to-end data flow efficiently. ### 3. Flexibility and Power for Complex Workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Akash M. | Senior Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** May 03, 2026 **What do you like best about Apache Airflow?** I use Apache Airflow to schedule and manage data pipelines, and I appreciate how it automates ETL pipelines easily and enables monitoring of tasks. I love its flexibility to build and manage complex workflows effortlessly using code, along with features like Operators and sensors. The UI is very helpful for tracking pipelines and quickly debugging failures. It helps me create, automate, and schedule complex workflows by reducing manual efforts, making pipeline monitoring and error handling much easier. I love most of the features that Airflow provides, and I rate it 9/10 as I love this platform. **What do you dislike about Apache Airflow?** For beginners, Apache Airflow can be complex to setup, especially with Docker. At times when a DAG is updated, the UI can feel slow. **What problems is Apache Airflow solving and how is that benefiting you?** I use Apache Airflow to automate ETL pipelines, reducing manual efforts and making pipeline monitoring and error handling easier. ### 4. Organised, Flexible Workflow Management with a Great Monitoring UI **Rating:** 5.0/5.0 stars **Reviewed by:** Brian K. | Technical Lead, Mid-Market (51-1000 emp.) **Reviewed Date:** March 22, 2026 **What do you like best about Apache Airflow?** What I like most about Apache Airflow is how it makes managing workflows feel organised and predictable. You can clearly define tasks, set dependencies, and see everything laid out in one place, which makes complex pipelines much easier to understand. I also like how flexible it is. You can build and customise workflows to fit pretty much any data pipeline, and the scheduling just works once it is set up. The UI is another big plus, it makes it easy to monitor runs, debug issues, and quickly see where something has failed without digging through logs for ages. **What do you dislike about Apache Airflow?** One thing I don’t like about Apache Airflow is that it can feel quite heavy and complex, especially at the start. Setting it up properly takes time, and there are quite a few moving parts to understand before everything runs smoothly. Debugging can also be frustrating at times. When something fails, the logs are not always the easiest to follow, so it can take longer than expected to figure out what went wrong. It can also be a bit resource intensive, which is not ideal if you are running smaller projects or just need something lightweight. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves the problem of managing and scheduling complex workflows in a clear and structured way. Instead of running scripts manually or relying on fragile cron jobs, it lets you define everything as a pipeline with dependencies, retries, and proper monitoring. For me, that means my data pipelines run automatically and reliably without constant oversight. It also makes it much easier to track what is happening, catch failures early, and fix issues quickly. Overall, it saves time and gives me confidence that my workflows are running as expected. ### 5. Powerful Orchestration for Complex Data Pipelines with Great Community Support **Rating:** 4.5/5.0 stars **Reviewed by:** Aindrila R. | Assistant System Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** January 03, 2026 **What do you like best about Apache Airflow?** For me, the standout feature is definitely the Web UI. As a data engineer, I often find myself troubleshooting, and the Grid view in Airflow makes it remarkably simple to identify exactly where a pipeline has failed. I can quickly access the logs for any specific task and determine what went wrong within seconds. This level of transparency is something that traditional cron jobs or basic scripts simply don't offer. Having a central dashboard for all your workflows truly provides peace of mind. **What do you dislike about Apache Airflow?** The main challenge is the ease of implementation for beginners. Setting up the infrastructure (like the webserver, scheduler, and database) requires a good bit of DevOps knowledge, which can be a hurdle for a small team. Because it’s open-source, you don’t have traditional customer support, so you rely heavily on the community. While the community is active, the documentation can sometimes be a bit overwhelming when you are trying to troubleshoot a very specific configuration issue. It’s a powerful tool, but the ease of use from a setup perspective could definitely be improved. **What problems is Apache Airflow solving and how is that benefiting you?** Before adopting Apache Airflow, our team faced significant challenges managing the complex dependencies among our various data scripts. We relied on simple cron jobs and manual triggers, which meant we often wouldn't discover a failed transformation until long after the fact. Airflow has addressed these issues by serving as our central 'source of truth' for automation. It orchestrates the sequencing of our tasks seamlessly, ensuring that Step B only begins once Step A has completed successfully. Personally, I've found that Airflow has greatly reduced the amount of time I spend on manual monitoring and troubleshooting. Rather than sifting through server logs to track down errors, I can simply consult the Airflow dashboard to pinpoint exactly where a task has failed. This shift has allowed me to dedicate more time to developing new data pipelines instead of just maintaining existing ones, resulting in a workflow that is both more reliable and scalable. ### 6. Flexible Workflow Management with Apache Airflow **Rating:** 5.0/5.0 stars **Reviewed by:** Atharva P. | Cloud BI Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2026 **What do you like best about Apache Airflow?** What I like best about Apache Airflow is its flexibility in building and managing complex workflows using code. The DAG-based approach makes it easy to define dependencies, schedule jobs, and monitor pipeline execution in a centralized way. **What do you dislike about Apache Airflow?** Debugging failed workflows and managing dependencies across multiple tags can become challenging over time. Also, initial setup and maintenance can be complex, especially for large-scale deployments if it is not a managed service. Basic local setup is manageable, but production-grade deployment with scaling, monitoring, and high availability requires significant expertise. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves our workflow orchestration and scheduling challenges, automating workflows, triggering dependent jobs, managing retries, and coordinating data movement. This reduces manual intervention and improves pipeline reliability. ### 7. Streamlined ETL with Powerful DAG Visualization **Rating:** 5.0/5.0 stars **Reviewed by:** Dhanush R. | Senior Technical Customer Success Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Apache Airflow?** I love using Apache Airflow for creating ETL pipelines, especially with its UI-driven DAG visualization that makes understanding the workflow and dependencies so much easier. The graph and stage view are amazing, and identifying errors is straightforward. The task monitoring feature is really useful, letting me see which jobs are running, and the retry and failure handling are essential for smooth operations. Setup was straightforward, particularly on AWS, and it's incredibly helpful when integrated with Acceldata for pipeline observability. **What do you dislike about Apache Airflow?** The only thing I noticed, when there are more DAGs, is that the UI can sometimes feel slow when dealing with many tasks. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow helps us create a robust ETL pipeline, automates Spark job flows, and simplifies error identification with its UI. The DAG visualization aids in understanding workflows and dependencies. ### 8. industry standard for cron jobs **Rating:** 4.5/5.0 stars **Reviewed by:** Mitul C. | Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** January 27, 2026 **What do you like best about Apache Airflow?** the whole process is very extensible, since there are so many users, we have a lot of built in operators/plugins. the UI is very clean and intuitive. **What do you dislike about Apache Airflow?** i dont think there is a feature where we can watch the logs of all the jobs in one place, lets say we have 20-30 steps, so we have to go to each step and check the logs, so i would all logs to be consolidated **What problems is Apache Airflow solving and how is that benefiting you?** solves the problem of cron jobs and removes manual dependency. it is very reliable and any flaws also show up quite easily in the process if they do exist ### 9. Reliable Data Orchestration with Setup Challenges **Rating:** 4.5/5.0 stars **Reviewed by:** Raghavendra S. | Data enginner, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** December 13, 2025 **What do you like best about Apache Airflow?** I like Apache Airflow's clear DAGs since they make workflows easy to understand and maintain. The scheduling feature ensures pipelines run automatically without manual effort, which is really helpful. I also appreciate the retries and monitoring, as they help quickly detect and recover from failures. Additionally, its scalability is a significant advantage, allowing me to handle growing data workloads reliably, making Airflow dependable for production pipelines. Overall, these features really enhance my experience with Apache Airflow. **What do you dislike about Apache Airflow?** Some things in Apache Airflow not work very well for me. Setup and initial configuration is little complex and takes time. UI sometimes feels slow when lot of DAGs are running. Debugging failed tasks is not always clear, logs are scattered. Also version upgrades can break existing DAGs, backward compatibility should be better. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow manages and automates complex data workflows. It solves manual job running, tracks dependencies, and handles failures across systems. I can define task dependencies, schedule workflows, monitor executions, and fix failures, providing structure, visibility, and reliability to data orchestration. ### 10. Powerful workflow orchestration tool with great flexibility **Rating:** 5.0/5.0 stars **Reviewed by:** Aditya R. | Sofware Development Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2025 **What do you like best about Apache Airflow?** Apache Airflow makes it incredibly easy to design, schedule, and monitor complex workflows using Python. I like how it allows building DAGs in a very readable and modular way, which helps in managing large-scale data pipelines. The UI is intuitive and gives full visibility into task execution, retries, and logs. Its ability to integrate seamlessly with databases, cloud providers, and external services makes it very flexible for real-world use cases. The community support and available plugins also make it easy to extend functionality as needed. Customer Support is also good. **What do you dislike about Apache Airflow?** Airflow can be a bit challenging to set up and configure initially, especially when deploying in production with multiple workers and schedulers. Resource management and scaling sometimes require additional tuning, and debugging can be tricky for new users. The learning curve is steeper compared to some other orchestration tools, and the UI, while useful, could be more modern and responsive. However, once set up, it becomes stable and very reliable. **What problems is Apache Airflow solving and how is that benefiting you?** At my company, Apache Airflow has become the backbone of our automation and data workflows. Some examples of how we use it: Integration Management DAG: Automates the client ID and secret token lifecycle. Since tokens expire every three months, Airflow ensures timely reminders via email (daily during the last week before expiry) without any manual follow-up. Data Purging DAG: Handles automatic purging of client data after a configured time, which varies across clients. This helps us meet compliance requirements and optimize storage without manual monitoring. Data Migration DAG: Manages migration between hot and cold storage, ensuring cost-efficient and optimized data management. By orchestrating these processes in Airflow, we’ve significantly reduced manual overhead, ensured reliability with retries and alerts, and built a scalable workflow system that adapts to multiple client needs. ### 11. Powerful Task Scheduler with Installation Challenges **Rating:** 4.0/5.0 stars **Reviewed by:** Shabbir P. | Senior Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** December 11, 2025 **What do you like best about Apache Airflow?** I use Apache Airflow for project flow management and monitoring. I find its web-based UI and Python scripting features valuable, making it easy to develop and design process flows. Python as a scripting language is more user-friendly than other complex languages, which helps in writing complex flowcharts better than with traditional languages. **What do you dislike about Apache Airflow?** The installation process of Apache Airflow is quite complex and highly dependent on PIP, making it very difficult to handle on cyber security blocked servers. The initial setup is challenging, especially in a proxy-based environment, as it requires lots of permissions and manual installations. **What problems is Apache Airflow solving and how is that benefiting you?** I use Apache Airflow for project flow management and task scheduling. It's a tool that simplifies developing and designing process flows with its web-based UI and Python scripting, which I find user-friendly and effective for writing complex flow charts. ### 12. Streamlining Data Pipelines with Apache Airflow **Rating:** 4.0/5.0 stars **Reviewed by:** Bikash s. | DevOps Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** August 02, 2025 **What do you like best about Apache Airflow?** What I like best about Apache Airflow is its flexibility and powerful scheduling capabilities. As a developer, I can design complex workflows as code using Python, making it easy to version-control and collaborate with teammates. The UI is intuitive for monitoring DAG runs and troubleshooting issues, and Airflow’s large ecosystem of integrations lets me connect with almost any tool or database **What do you dislike about Apache Airflow?** The learning curve is pretty steep, particularly when configuring the scheduler and managing task dependencies. Sometimes Airflow’s web UI feels sluggish, and troubleshooting issues can get complicated with complex DAGs. Also, while there are a lot of integrations, keeping dependencies compatible during upgrades isn’t always smooth. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves the challenge of orchestrating and automating complex data workflows. Before Airflow, I had to manage ETL pipelines as messy scripts or manual cron jobs, which made them hard to maintain, debug, or scale. With Airflow, I define my workflows as code (DAGs), so the logic is modular, testable, and much easier to monitor or fix if something goes wrong. This means faster development, better reliability, and less time spent on repetitive maintenance. ### 13. Pipeline and user management at the most **Rating:** 5.0/5.0 stars **Reviewed by:** Pedro P. | Visiting Professor, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** August 04, 2025 **What do you like best about Apache Airflow?** Airflow enhances pipeline observability - both process and data - to the highest level. It enables the distribution of pipeline execution among a team of stakeholders with varying technical backgrounds in a safe and user-friendly environment. **What do you dislike about Apache Airflow?** The installation, setup, and running are not straightforward, and some fine-tuning is necessary. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow provides comprehensive pipeline transparency (both code and data) while enabling secure distribution of pipeline execution and monitoring among stakeholders with diverse technical backgrounds. ### 14. Streamlining Supply Chain Workflows with Apache Airflow **Rating:** 5.0/5.0 stars **Reviewed by:** Abhishek K. | Senior Analyst, Retail, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2025 **What do you like best about Apache Airflow?** What I like best about Apache Airflow is how it lets me orchestrate complex data pipelines in a very structured way. In supply chain demand planning, we deal with multiple data sources – sales, inventory, production, even external signals like holidays or weather. Airflow makes it easier to schedule, monitor and re-run these workflows without too much manual hassle. I also like the visibility it gives through the UI, it helps to quickly catch when a task is failing and why. For me, this saves a lot of time compared to writing adhoc scripts and cron jobs. **What do you dislike about Apache Airflow?** Sometimes Airflow can feel a bit heavy, specially when setting it up the first time. For smaller workflows it almost feels like an overkill, but in larger supply chain planning projects it pays off. The UI is good but can be slow when you have too many DAGs running. Also, learning curve is not trivial – it takes some time to get comfortable with operators, connections and handling backfills. I also wish the documentation had more real-world supply chain use cases instead of just generic ETL examples. **What problems is Apache Airflow solving and how is that benefiting you?** In my case, Airflow is solving the problem of reliably moving models from experimentation into production. In supply chain demand planning, I often need to retrain forecasting models on new sales and inventory data every few days. Airflow lets me automate this entire pipeline – from data extraction, cleaning, model training, evaluation, and final deployment into dashboards or APIs. This reduces manual steps and chances of mistakes. The biggest benefit is consistency – my models are always refreshed with the latest data without me babysiting the process. It also helps my team track failures quickly and re-run only the failed steps instead of the whole workflow, which saves us a lot of time. ### 15. Effortless ETL Setup with Broad Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Akash B. | Software Engineer 3, Enterprise (> 1000 emp.) **Reviewed Date:** November 26, 2025 **What do you like best about Apache Airflow?** Setting up ETL pipelines and orchestrating workflows is straightforward, thanks to the wide range of integrations available with nearly every data source and enterprise application. **What do you dislike about Apache Airflow?** While there is a wide range of possible integrations, the built-in scheduler is not particularly advanced when it comes to managing complex scheduling requirements. **What problems is Apache Airflow solving and how is that benefiting you?** This is a straightforward DAG orchestrator that allows you to create multistep workflows, making it easier to break down complex tasks into simpler steps and schedule them efficiently. ### 16. Orchestrating ETL jobs made easy with Airflow. **Rating:** 5.0/5.0 stars **Reviewed by:** Yanamala P. | Software Engineer Intern, Mid-Market (51-1000 emp.) **Reviewed Date:** June 07, 2025 **What do you like best about Apache Airflow?** Apache Airflow is very much helpful in orchestrating complex work flows. I really love the DAG based workflow orchestration, this helped me in breaking down large tasks into smaller tasks which made debugging easy. The best thing I like about airflow is its retry mechanism, If I want to run a specific task of a Dag or Dag failed at a particular task then I can just retry at the specific task instead of running entire Dag from start which really saved a lot of time. One more best thing about Airflow is its Dynamic Dag approach, When there is requirement to create multiple similar DAGs then we can create a specific template and use that template for all the similar DAGs which is really an amazing feature, this helped me a lot and reduced manual writing. I have been using Airflow for 1 Year and I feel that Airflow is the best platform for Orchestration Workflows. Customer support is very responsive and helpful. **What do you dislike about Apache Airflow?** There is no proper documentation for some Operators which makes difficult for new users. **What problems is Apache Airflow solving and how is that benefiting you?** Airflow helped me in Orchestrating Pipelines easily and schedule those pipelines to run at specific time. There are many useful operators provided by airflow that made transformations effective and simple. FTP operator has benefited me a lot while working with FTP Sources and we can also define custom Operators according to our requirement. ### 17. Airflow vs Cron: When Simplicity Matters **Rating:** 3.5/5.0 stars **Reviewed by:** Saketh K. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** July 25, 2025 **What do you like best about Apache Airflow?** Opensource, UI to track almost every aspect of each job, python friendly. **What do you dislike about Apache Airflow?** While Apache Airflow is powerful, it often complicates simple tasks with added abstractions like custom directives and inter-task communication. Job scheduling isn’t intuitive—requiring attention to interval ends—and log loading can be sluggish. Though opinions may vary, I personally find traditional cronjobs a simpler and more effective solution for managing a large number of jobs. **What problems is Apache Airflow solving and how is that benefiting you?** To manage multiple jobs, track them, see the failed jobs, re-run the failed jobs, analyzing the logs to check why a job has failed - everything using Airflow UI. One of my teammates wanted us to try Airflow if it can manage our production jobs, it did, but at a higher than expected cost: infra on cloud + human hours. ### 18. Airflow Orchestration of various Data Pipelines **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** December 29, 2025 **What do you like best about Apache Airflow?** Airflow has capabilities that overcomes traditional CRON jobs sscheduling, I have been using it since last 6+ years and it is helping me a lot to built robust pipelines with ease in backfilling tasks when any issue comes in. Also it has a good customer support when it comes to its upgradation **What do you dislike about Apache Airflow?** I do not dislike anything in terms of Airflow as it is already a best tool for data engineers and AI data engineers.It provides seamless integration. **What problems is Apache Airflow solving and how is that benefiting you?** I had nuerous problems in data pipelines scheduling, earlier there were overheads and manual config configuration referencing issues in python code. The xcom had helped a lot with python and bash operators to schedule jobs with each and push details to downstream data pipeline tasks. ### 19. Great DAG Frontend, But Feels Outdated **Rating:** 3.5/5.0 stars **Reviewed by:** Tobias S. | Sr. BI Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** October 15, 2025 **What do you like best about Apache Airflow?** It has a nice user interface for viewing the status of DAGs, which has become an industry standard. Additionally, when jobs fail, the logs are very helpful for tracking down what went wrong. **What do you dislike about Apache Airflow?** The setups feel outdated and unnecessarily complex. In comparison, tools such as dbt, and especially Databricks, have made significant improvements recently. **What problems is Apache Airflow solving and how is that benefiting you?** ETL Orchestrations are essential for managing and automating data workflows. They help streamline the process of extracting, transforming, and loading data, making it easier to handle complex data pipelines. ### 20. Powerful and flexible workflow orchestration tool **Rating:** 5.0/5.0 stars **Reviewed by:** Rahul D. | Program Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** August 13, 2025 **What do you like best about Apache Airflow?** Apache Airflow offers excellent flexibility in defining, scheduling, and monitoring complex workflows. The DAG-based approach is intuitive for data engineers, and the extensive operator ecosystem allows easy integration with various systems. Its UI makes tracking and debugging workflows straightforward, and its scalability ensures smooth operation even with large pipelines. **What do you dislike about Apache Airflow?** The initial setup and configuration can be challenging, especially for beginners. Managing dependencies and scaling in production requires strong infrastructure knowledge. Some tasks may require custom operators or plugins, which can be time-consuming to develop. The web UI, while functional, could benefit from more modern UX improvements. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow is helping orchestrate complex AI and data workflows involving multiple dependent steps across different systems. It provides a centralized platform to schedule, monitor, and manage pipelines for data preprocessing, model training, and deployment. This reduces manual intervention, improves reliability, and ensures reproducible execution. By automating these workflows, it accelerates delivery, reduces operational errors, and enables the team to focus more on developing models rather than managing infrastructure. ### 21. Powerful Workflow Orchestration with Flexibility and Scalability **Rating:** 5.0/5.0 stars **Reviewed by:** Nirbhay K. | Customer Support Operations Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** August 12, 2025 **What do you like best about Apache Airflow?** Apache Airflow excels in orchestrating complex workflows with ease. Its DAG-based approach makes task dependencies clear and manageable. The web UI is intuitive for monitoring and debugging jobs, and the integration options with cloud services and databases are extensive. Being open-source, it has strong community support and frequent updates, making it adaptable to evolving needs. Scalability is another plus — it can handle everything from small pipelines to enterprise-scale workloads efficiently. **What do you dislike about Apache Airflow?** The initial setup and configuration can be challenging for beginners, especially when deploying in a distributed environment. Documentation, while extensive, can sometimes be overwhelming or outdated. Resource usage can become heavy for very large DAGs, requiring careful optimization. Additionally, the learning curve for custom operators and plugins can be steep for new developers. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow helps us orchestrate and automate complex data pipelines, ensuring that dependencies are managed seamlessly. It has significantly reduced manual intervention in ETL processes, improved data reliability, and provided clear visibility into task execution. With its scheduling capabilities, we can run workflows at scale and ensure timely delivery of data for analytics and reporting, which improves decision-making across the organization. ### 22. Streamlining Complex Data Pipelines with Ease **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Small-Business (50 or fewer emp.) **Reviewed Date:** August 11, 2025 **What do you like best about Apache Airflow?** What I like best about Apache Airflow is its ability to orchestrate complex workflows with clear visibility and control. The DAG-based structure makes it easy to design, monitor, and modify data pipelines, while the scheduler ensures tasks run reliably and in the right sequence. Its modularity and integration capabilities with various data sources and tools make it extremely versatile. The web UI is also a huge plus, as it provides real-time monitoring and quick debugging, which saves time during development and production. **What do you dislike about Apache Airflow?** While Apache Airflow is powerful, it does come with a steep learning curve for beginners, especially when setting up and configuring it for the first time. Deployments can be complex, and managing dependencies across environments requires careful attention. Additionally, for smaller projects, the overhead of running and maintaining Airflow can feel heavier compared to lightweight alternatives. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow helps us automate, schedule, and monitor complex data workflows, reducing manual intervention and minimizing errors in data processing. It enables reliable orchestration of ETL pipelines, ensuring data arrives on time for analytics and reporting. This has improved operational efficiency, reduced downtime, and provided greater transparency through real-time monitoring. Given the community’s active development and focus on scalability, I believe Airflow is headed in the right direction, with ongoing enhancements that make it more user-friendly and robust for large-scale data environments. ### 23. Using Apache Airflow to orchestrate pipeline workflow for Databricks and EMR jobs **Rating:** 4.0/5.0 stars **Reviewed by:** Mohammad M. | Senior System Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** August 11, 2025 **What do you like best about Apache Airflow?** Very easy to understand and use It is very good for defining complex workflows as code has very good monitoring/observability features Best part is we don't have to manage any infra if we use services like AWS MWAA for apache airflow. Very easy to implement. Has good customer support via email or support tickets We use it day in day out few projects for managing workflow for bedrock for using AI integrations, databricks and emr. We use it along with AWS S3, Bedrock and Postgres SQL and Github **What do you dislike about Apache Airflow?** It doesn't have any tracking mechanism and makes it hard to track any changes made or revert back a version of code Doesn't support live streaming processing **What problems is Apache Airflow solving and how is that benefiting you?** We use it to automate RAG workflow which retrieve information from our database and send it to bedrock knowledge base for input processing and generating output. We also use it as orchestrator for pulling and pushing data from and to Databricks and EMR. While using S3 as storage. ### 24. Powerful Workflow Automation with Some Learning Curve **Rating:** 5.0/5.0 stars **Reviewed by:** Usman M. | Backend Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 10, 2025 **What do you like best about Apache Airflow?** Apache Airflow excels in workflow automation and scheduling, making it ideal for complex data pipelines. Key strengths: Flexibility: Define workflows as code (Python) for full customization. Scalability: Handles large workflows with distributed execution (e.g., Celery/Kubernetes). Extensibility: Rich library of operators/integrations (AWS, GCP, Snowflake, etc.). UI/Visibility: Intuitive dashboard for monitoring DAGs (Directed Acyclic Graphs) and task statuses. Community/Open Source: Active community and frequent updates. **What do you dislike about Apache Airflow?** While powerful, Airflow has drawbacks: Steep Learning Curve: New users struggle with concepts like DAGs, XComs, and executors. Complex Setup: Local deployment (e.g., Docker/Celery) can be tricky; managed services (Astro, MWAA) simplify this. Limited Real-Time Processing: Designed for batch workflows, not streaming. Debugging: Logs can be fragmented, and dynamic pipeline generation is unintuitive. Scaling Costs: Self-hosted clusters require significant DevOps effort. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow addresses: Workflow Automation: Streamlines scheduling/monitoring of complex data pipelines (e.g., ETL, ML model training). Dependency Management: Ensures tasks run in the correct order with retries for failures. Cross-Tool Coordination: Integrates with databases (PostgreSQL), cloud services (AWS S3, GCP BigQuery), and ML tools (TensorFlow, PyTorch). Reproducibility: Code-defined workflows (DAGs) enable version control and collaboration. Benefit: Saves engineering time, reduces manual errors, and scales with our data infrastructure. ### 25. Airflow makes my data tasks so much easy **Rating:** 4.5/5.0 stars **Reviewed by:** Atin K. | Senior Analyst (Planning and Replenishment), Mid-Market (51-1000 emp.) **Reviewed Date:** August 12, 2025 **What do you like best about Apache Airflow?** I like that it makes all my data jobs run on time without me sitting and doing everything manualy. The schedualing is super helpful and once I set it up, it just works in the background. Makes my work alot easier. **What do you dislike about Apache Airflow?** Sometimes setting it up in the start feels a bit confusing, and if something breaks it can take time to figure out why. The UI also feels a bit old, wish it was more simple to use. **What problems is Apache Airflow solving and how is that benefiting you?** It helps me connect all my ML steps together, like data cleaning, training the model, and testing, without me doing it one by one. Once I make the pipeline, it runs automaticly and saves me so much time. It also makes sure everything runs in the right order so I dont miss any step. ### 26. Powerful and intuitive application, but the updates are too frequent **Rating:** 5.0/5.0 stars **Reviewed by:** Angelo C. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** October 30, 2025 **What do you like best about Apache Airflow?** A powerful and intuitive application that allows you to perform many tasks and schedule automations, all through a simple, clean, and modern interface. **What do you dislike about Apache Airflow?** Installation and updates can often be cumbersome. Updates are too frequent and occasionally introduce minor bugs. **What problems is Apache Airflow solving and how is that benefiting you?** It supports everything from data pipeline management, file automations, and integrations to scheduled reports and much more. There are all kinds of available connections, various types of SQL, SMTP mail, Samba, SFTP, and others. It solves many orchestration-related and integration issues. ### 27. Powerful workflow orchestration with room for usability improvements **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Electrical/Electronic Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** August 13, 2025 **What do you like best about Apache Airflow?** Apache Airflow is extremely powerful for orchestrating complex workflows and scheduling tasks across various systems. Its DAG-based approach offers excellent visibility and control over dependencies. The wide range of integrations and plugins makes it highly adaptable, while the active open-source community ensures continuous improvements and resources for troubleshooting. **What do you dislike about Apache Airflow?** The initial setup and configuration can be complex, especially for newcomers. The learning curve is steep, and performance tuning for large-scale workflows often requires significant expertise. The UI, while functional, can sometimes feel outdated and less intuitive compared to modern workflow tools. Additionally, upgrading to newer versions may require careful migration planning to avoid breaking changes. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves the challenge of automating, scheduling, and monitoring complex workflows that involve multiple interdependent tasks. It provides a centralized platform to manage ETL pipelines, data processing jobs, and machine learning workflows, ensuring tasks run in the right order with proper error handling. This has significantly reduced manual intervention, improved reliability, and provided real-time visibility into job status, enabling faster troubleshooting and more efficient resource utilization. ### 28. Apache Airflow is a great tool **Rating:** 4.0/5.0 stars **Reviewed by:** Sarthak M. | Business Technology Solutions Associate Consultant, Enterprise (> 1000 emp.) **Reviewed Date:** July 26, 2025 **What do you like best about Apache Airflow?** Apache Airflow makes managing complicated data tasks much easier being able to code workflows in Python is great, and the UI helps me spot issues fast. Love how it connects with everything and the built-in tools save time. **What do you dislike about Apache Airflow?** Advanced setup (scaling, security) can be tricky The UI gets cluttered with too many workflows **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow has been a game-changer for orchestrating our complex data pipelines. I appreciate how it enables me to automate, schedule, and monitor workflows with ease. ### 29. Apache Airflow Analytics **Rating:** 3.0/5.0 stars **Reviewed by:** Majid H. | Senior Business Intelligence Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** August 09, 2025 **What do you like best about Apache Airflow?** Scheduling daily/weekly/monthly data reports. **What do you dislike about Apache Airflow?** Can be demanding on CPU/memory for large deployments **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves workflow and pipeline during data engineering process , it makes it easier to define, schedule, and monitor complex data pipelines. ### 30. Powerful Workflow Orchestration, but Needs a Bit of Setup Effort **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** August 11, 2025 **What do you like best about Apache Airflow?** The best thing about Apache Airflow is its flexibility and scalability in orchestrating complex workflows. **What do you dislike about Apache Airflow?** A common dislike about Apache Airflow is that it has a steep learning curve and requires significant setup and maintenance for production use. It takes time to master it **What problems is Apache Airflow solving and how is that benefiting you?** It solves the problem of managing, scheduling, and monitoring complex, multi-step workflows. It benefits me in so many ways- 1. Automates repetitive processes so you don’t have to run tasks manually. 2.Ensures tasks run in the correct order with built-in dependency management. 3. Makes it easy to monitor job status, get alerts on failures, and retry tasks automatically. ### 31. ETL for data workflows and heavy data engineering ops **Rating:** 5.0/5.0 stars **Reviewed by:** Aditya K. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** August 02, 2025 **What do you like best about Apache Airflow?** Platforms like AWS and GCP charge for the ETL workflows where as Apache Airflow is easy to use and host on standalone server or even on Kubernetes **What do you dislike about Apache Airflow?** When hosted on kubernetes it slows down as kubernetes pod schedulers are not meant for memory intensic ops **What problems is Apache Airflow solving and how is that benefiting you?** We run heavy data operations like processing data produced after running spark jobs and EMR clusters and store them on snappy parquet format ### 32. ETL jobs made easier **Rating:** 5.0/5.0 stars **Reviewed by:** Debishree T. | Software Engineer (SRE/Devops), Small-Business (50 or fewer emp.) **Reviewed Date:** August 02, 2025 **What do you like best about Apache Airflow?** We use Apache airflow to process realtime data in our data lake and to run batch jobs in a workflow **What do you dislike about Apache Airflow?** At times it becomes little tricky to run ETL for realtime data apps **What problems is Apache Airflow solving and how is that benefiting you?** We have our own data lake in AWS s3 and apache airflow makes it easier to collect data from s3 and process it in a workflow ### 33. Best multi purpose automation app **Rating:** 5.0/5.0 stars **Reviewed by:** MAHANTESH S H. | Lead Cybersecurity, Enterprise (> 1000 emp.) **Reviewed Date:** August 12, 2025 **What do you like best about Apache Airflow?** The ability to schedule multiple automated flows from different tools like nessus, open vas and to get the results at one place **What do you dislike about Apache Airflow?** UI could be better. With detailed guide missing **What problems is Apache Airflow solving and how is that benefiting you?** It solves the hassle of navigating to different apps and the manual effort of checking the outputs in all these different consoles ### 34. Wonderful orchestration tool **Rating:** 4.5/5.0 stars **Reviewed by:** Dwarikanath P. | Consultant, Enterprise (> 1000 emp.) **Reviewed Date:** August 08, 2025 **What do you like best about Apache Airflow?** ability to manage dependencies, handle retries, and provide detailed insights into workflow execution through robust monitoring and logging capabilities. **What do you dislike about Apache Airflow?** Difficult to install in windows operating **What problems is Apache Airflow solving and how is that benefiting you?** Airflow eliminates the need for manual triggering and monitoring of tasks, which can be prone to human error and inefficiency, especially in complex data pipelines. ### 35. The interface and understanding **Rating:** 4.5/5.0 stars **Reviewed by:** Suneel P. | Associate Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** July 25, 2025 **What do you like best about Apache Airflow?** The main best of airflow is it's a friendly to understand and easy to work on even with basic knowledge **What do you dislike about Apache Airflow?** Not much is there, the changes are take time to reflect in the airflow **What problems is Apache Airflow solving and how is that benefiting you?** The logs ### 36. Powerful open source workflow Orchestration tool **Rating:** 5.0/5.0 stars **Reviewed by:** Priyanka M. | Senior Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** July 30, 2025 **What do you like best about Apache Airflow?** Lot of features to use and very helpful for processing batch data. **What do you dislike about Apache Airflow?** There is learning curve, python and DAG knowledge is required. **What problems is Apache Airflow solving and how is that benefiting you?** We are processing batch data stored in orc files for deduplication. ### 37. Good to handle daily automation **Rating:** 5.0/5.0 stars **Reviewed by:** Jayesh T. | QA automation engineer, Financial Services, Mid-Market (51-1000 emp.) **Reviewed Date:** August 12, 2025 **What do you like best about Apache Airflow?** Good product to automate daily regression work **What do you dislike about Apache Airflow?** Sometimes it fails to automate daily regression work **What problems is Apache Airflow solving and how is that benefiting you?** Good to use ### 38. Best orchestration Tool **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** August 09, 2025 **What do you like best about Apache Airflow?** The connectivity, scheduling options , easier to operate the UI **What do you dislike about Apache Airflow?** Nothing much. Very much user friendly to use **What problems is Apache Airflow solving and how is that benefiting you?** It helps to schedule your pipelines in azure AWS and gcp and helps you load data to your target tables ### 39. Apache Airflow : A must learn orchestration tool for data geeks. **Rating:** 4.0/5.0 stars **Reviewed by:** Digamber K. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 18, 2024 **What do you like best about Apache Airflow?** Airflow provides numerous cross-platform integration with almost all the required technologies. It has vast number of features while creating DAG's. I really loved the ideas of new release wrt object storage, easily managable platform, new operators like FTP, FTPs and custom operators. UI is bit clumsy but altogether airflow is very easy to use and implement ETL pipelines. I have been using Airflow for last 1 year and the improvement they show is very promising. **What do you dislike about Apache Airflow?** UI is clumsy. In order to see the task in UI I have to go back to all dags when we retrigger 2nd time. It can be better but it compensates with the feature it has. **What problems is Apache Airflow solving and how is that benefiting you?** We are using airflow for orchestrating and managing the ETL workflow. We wanted to have a data driven behaviour which is feasible and efficient using Airflow. Airflow provides tons of integration with other latest technologies. ### 40. Data Pipeline Orchestration tool **Rating:** 4.5/5.0 stars **Reviewed by:** Sumit G. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 08, 2025 **What do you like best about Apache Airflow?** - Integration - Scalability - Performance - Data Pipeline Management - Data Pipeline Root Cause Analysis (RCA) - Alert System - Easy Installation - Use more than 5 hours in a day **What do you dislike about Apache Airflow?** - Airflow UI/ User Experience - Pipeline Execution grid view - Dag Graph **What problems is Apache Airflow solving and how is that benefiting you?** In Airflow version 2.1.4, I used the GlueJobOperation. When verbose=True, it was not able to provide Glue job logs. We fixed this issue and are now able to access logs in this version. ### 41. Apache Airflow **Rating:** 5.0/5.0 stars **Reviewed by:** Ashutosh R. | Data Engineer III, Enterprise (> 1000 emp.) **Reviewed Date:** August 02, 2024 **What do you like best about Apache Airflow?** The best thing about Apache Airflow is that it provides integration with various services like big query , AWS , GCP etc.Plus it is available as as service in all cloud provides which provides seamless experience.The User Experience is perfect. **What do you dislike about Apache Airflow?** Sometimes we face problem when there are two many task in a single airflow instance which requires more number of executors. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow solves the problem of scheduling and orchestration with a good user experience ### 42. Suitable for setting up ETLs and Cron Jobs Quickly and Easily **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** March 29, 2024 **What do you like best about Apache Airflow?** I like how simple it is to setup and get started with Apache Airflow. As it is backed by Python as a programming language ETLs and other kind of Jobs are very easy and quick to code and deploy. It's tedious otherwise to setup these things and takes much experience. But with Apache Airflow one can spin up new jobs quickly with tools that help with troubleshooting and fast debugging. **What do you dislike about Apache Airflow?** One thing that I would love Apache Airflow to have would be some improvements to scale the ETLs and provide tools that can help with smooth api integration in an enterprise level ecosystem. With increasing business requirements its kind of get tricky to manage. **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow saved me alot of time when setting up ETLs and Cronjobs required for business initially. It saves alot of time with the tools it provides and is easier to code since its in python. ### 43. Airflow Review **Rating:** 4.0/5.0 stars **Reviewed by:** Neeraj G. | DotNet Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 29, 2024 **What do you like best about Apache Airflow?** It has a lot of integrations with n number of platforms **What do you dislike about Apache Airflow?** There should be more work done on user interface. **What problems is Apache Airflow solving and how is that benefiting you?** It helps is easy orchestration of ETL pipelines and connecting to various diffrent technologies in a single platform. It saves a lot of time. ### 44. automate reporting and data pulls **Rating:** 4.0/5.0 stars **Reviewed by:** Tong Yi C. | Senior Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** May 21, 2024 **What do you like best about Apache Airflow?** being able to use python to create workflows that integrate with our reports is so core to many of our processes **What do you dislike about Apache Airflow?** errors and bug fixes are still manual and does take a while to troubleshoot **What problems is Apache Airflow solving and how is that benefiting you?** automation of workflows into our data lake and dashboards ### 45. Amazing for daily tasks **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Small-Business (50 or fewer emp.) **Reviewed Date:** October 12, 2023 **What do you like best about Apache Airflow?** Airflow is the most intuitive interface for setting up daily workflow jobs that I've come across. The API's are mostly easy to learn/use and it's all I love that it's all in Python. There are a few people on my team who are not trained programmers but they have figured out how to create simple daily jobs. The web interface is can be a bit obtuse but it gets the job done. Using the workflow visualizer makes debugging complex jobs much easier. **What do you dislike about Apache Airflow?** I wish it were easier to set up jobs that can be manually triggered. It can technically be done but the interface is clunky and lacks some basic quality-of-life features. The only complaint I have with the actual coding is that Jinja is hard to learn and debugging it can be a nightmare. That being said, if you stay within the straight-forward use cases, you shouldn't have any issues. **What problems is Apache Airflow solving and how is that benefiting you?** Apache handles all of our daily jobs in a much better way than Jenkins used to. It's easier to chain jobs together and see where the failure points are when edge cases arise. ### 46. best orchestrator in the market , with a huge community and active developments **Rating:** 5.0/5.0 stars **Reviewed by:** Raghwendra S. | SDE 4, Enterprise (> 1000 emp.) **Reviewed Date:** December 06, 2023 **What do you like best about Apache Airflow?** It support , operators for almost every data engineering tool / framework. Highly scalable. **What do you dislike about Apache Airflow?** There is plenty of options for observability in airflow . If some more community grafana dashboards or best practices are provided will help even further **What problems is Apache Airflow solving and how is that benefiting you?** Apache Airflow if the backbone of our data pipelines , data platform and is helping us to keep huge number of pipelines working under SLA ### 47. Efficient and Reliable Workflow Orchestration with Apache Airflow **Rating:** 4.5/5.0 stars **Reviewed by:** Anurag J. | Associate software Architect , Enterprise (> 1000 emp.) **Reviewed Date:** May 31, 2023 **What do you like best about Apache Airflow?** 1) Workflow Orchestration: Apache Airflow provides a powerful framework for defining, scheduling, and executing complex workflows. 2) Workflow Orchestration: Apache Airflow provides a powerful framework for defining, scheduling, and executing complex workflows. 3)Monitoring and Alerting: Airflow provides a user-friendly web interface that allows users to monitor the status and progress of their workflows. 4)Active Community and Ecosystem: Apache Airflow has a vibrant and active open-source community. 5)Mature and Production-Ready **What do you dislike about Apache Airflow?** 1) Learning Curve: Apache Airflow has a steep learning curve, especially for users who are new to workflow orchestration concepts or Python programming. 2) Complexity for Simple Use Cases: Airflow's feature-richness and flexibility can sometimes feel overwhelming for simple use cases. **What problems is Apache Airflow solving and how is that benefiting you?** 1) Complex Workflow Management: Airflow helps manage and coordinate complex workflows by providing a centralized platform to define, schedule, and monitor tasks and dependencies 2) Dependency Management: Airflow handles dependency management between tasks, ensuring that each task is executed only when its dependencies are met. 3)Scheduling and Retry Mechanisms: Airflow offers robust scheduling capabilities, allowing users to define precise schedules for task execution, including interval-based schedules, cron-based schedules, or specific trigger-based schedules. 4)Monitoring and Alerting 5) Parallel Execution and Scalability ### 48. Best tool for data flow **Rating:** 4.5/5.0 stars **Reviewed by:** Farhan K. | Big Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 10, 2023 **What do you like best about Apache Airflow?** The flexibility and customizability when it comes to creating and scheduling data pipelines. Uses python, which is the most popular programming language in the world. **What do you dislike about Apache Airflow?** Not really meant for streaming applications but it can be set up for those. Has a bit of a learning curve compared to other solutions. It only supports python for creating dags **What problems is Apache Airflow solving and how is that benefiting you?** It's very flexible as a data flow creator and there are a lot of callback mechanisms available for alerting, and fallback mechanisms. It's great for data engineers which is my field. ### 49. 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. ### 50. 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 ## 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?section=pricing&secure%5Bexpires_at%5D=2026-06-01+12%3A26%3A09+-0500&secure%5Bsession_id%5D=bd7ee5ea-ef5e-4c3f-95aa-6136709a17f6&secure%5Btoken%5D=66a98c389085acef2c45a8ca84f9771e172c13bb3ec79abd0a4b423e25023f0b&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 (6,105 reviews) - [MuleSoft Anypoint Platform](https://www.g2.com/products/mulesoft-anypoint-platform/reviews) - 4.5/5.0 (629 reviews) - [Camunda](https://www.g2.com/products/camunda/reviews) - 4.5/5.0 (316 reviews)