Apache Airflow Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

There is no proper documentation for some Operators which makes difficult for new users. Review collected by and hosted on G2.com.

Opensource, UI to track almost every aspect of each job, python friendly. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The setups feel outdated and unnecessarily complex. In comparison, tools such as dbt, and especially Databricks, have made significant improvements recently. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.