Apache Airflow

Apache Airflow

Apache Airflow

46 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Apache Airflow	Score 8.6 out of 10	N/A	Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.	N/A

Pricing

Apache Airflow

Editions & Modules

No answers on this topic

Offerings

Pricing Offerings
Apache Airflow
Free Trial
No
Free/Freemium Version
Yes
Premium Consulting/Integration Services
No

Entry-level Setup Fee

No setup fee

Additional Details

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More Pricing Information

Community Pulse
	Apache Airflow
Considered Both Products	Apache Airflow AV Anshuman Varshney Engineering Manager Chose Apache Airflow Open source Easy to configure Easy to learn Incentivized Helpful? Verified User Anonymous Chose Apache Airflow Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the … Incentivized Helpful? VT Victor Tay Engineer Chose Apache Airflow Using Jenkins and Kafka, it is not for the same purpose, although it might be similar. I would say AirFlow is really what it says on the can - workflow management. For our organisation, the purpose is clear. So long your aim is to have a rich workflow scheduler and job … Incentivized Helpful? Verified User Anonymous Chose Apache Airflow Apache Airflow is far superior! Incentivized Helpful? Nick Waters Solutions Engineer Chose Apache Airflow Much easy to deploy Apache Airflow as opposed to other products, with flexible deployment options as well as flexible integration with other tools and platforms. Incentivized Helpful? Alok Pabalkar Co-Founder & CTO Chose Apache Airflow digdag (https://www.digdag.io/)- Digdag is a very simple build, run, schedule, and monitor complex pipelines of tasks with a simple implementation and no configuration. Easy to write YAMLs Airflow has a better community and widely adopted. Has a better UI and better documentation Incentivized Helpful? Verified User Anonymous Chose Apache Airflow Overall using Apache Airflow is easy to use compare than other other tools available in the market, It is easy to integrate in apache airflow and the workflow can be monitored and scheduling can be done easily using apache airflow, recommend this tool for Automating the data … Incentivized Helpful? PRABHAT MISHRA Senior Devops Engineer Chose Apache Airflow Airflow was best suited in my use case for designing the ETL pipelines in a scripted manner for workflows & the UI was very good & easy to use. Incentivized Helpful? Verified User Anonymous Chose Apache Airflow There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a … Incentivized Helpful? Verified User Anonymous Chose Apache Airflow Step functions are only available in AWS but Apache Airflow provides cross cloud access. Apache Airflow also provides flexibility to pause, start and re-trigger dags. Provides executors where we can run in-house calculations if needed and which requires no integration with … Incentivized Helpful? RS Raghwendra Singh SDE 4 Chose Apache Airflow Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of … Incentivized Helpful?

Features

Apache Airflow

Workload Automation

Comparison of Workload Automation features of Product A and Product B
	Apache Airflow 9.8 Ratings 17% above category average
Multi-platform scheduling	10.00 Ratings
Central monitoring	10.00 Ratings
Logging	10.00 Ratings
Alerts and notifications	10.00 Ratings
Analysis and visualization	10.00 Ratings
Application integration	9.00 Ratings

Best Alternatives
	Apache Airflow
Small Businesses	No answers on this topic
Medium-sized Companies	ActiveBatch Workload Automation Score 7.5 out of 10
Enterprises	Control-M Score 9.3 out of 10
All Alternatives	View all alternatives

User Ratings
	Apache Airflow
Likelihood to Recommend	9.1 (0 ratings)
Usability	10.0 (0 ratings)

User Testimonials
	Apache Airflow
Likelihood to Recommend	For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback. Incentivized VT Victor Tay Engineer Read full review
Pros	Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines. Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic. Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability. Incentivized RS Raghwendra Singh SDE 4 Read full review
Cons	A local "dry run" or IDE plugin that can validate and simulate DAG execution without needing a full environment. Better feedback on DAG parse errors in the UI or CLI. Navigating large DAGs with hundreds of tasks can be slow and hard to understand visually. Incentivized Verified User Anonymous Read full review
Usability	For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows. Incentivized Verified User Anonymous Read full review
Alternatives Considered	Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support. Incentivized RS Raghwendra Singh SDE 4 Read full review
Return on Investment	Most of the ETL processes were automated, cutting down on human labor. Apache Airflow's user interface (UI) was very informative and straightforward. Since ETL processes were providing data via airflow, we were able to gain a deeper comprehension of the data at hand. Incentivized Verified User Anonymous Read full review
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