Top Rated Apache Airflow Alternatives
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
Video Reviews
A very handy tool for someone working in ETL & data engineering.
86 out of 87 Total Reviews for Apache Airflow
Overall Review Sentiment for Apache Airflow
Log in to view review sentiment.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
- Integration
- Scalability
- Performance
- Data Pipeline Management
- Data Pipeline RCA
- Alert System
- Esay Installation
- Use more than 5 hours in a day Review collected by and hosted on G2.com.
- Airflow UI/ User Experience
- Pipeline Execution grid view
- Dag Graph Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
Sometimes we face problem when there are two many task in a single airflow instance which requires more number of executors. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
being able to use python to create workflows that integrate with our reports is so core to many of our processes Review collected by and hosted on G2.com.
errors and bug fixes are still manual and does take a while to troubleshoot Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
It support , operators for almost every data engineering tool / framework. Highly scalable. Review collected by and hosted on G2.com.
There is plenty of options for observability in airflow .
If some more community grafana dashboards or best practices are provided will help even further Review collected by and hosted on G2.com.
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 Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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 Review collected by and hosted on G2.com.
