Apache NiFi vs. Apache Airflow

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Apache Airflow and Apache NiFi are, in fact, two whistles to a somewhat different tune. Still, you may be wondering which one is better suited for your expectations and goals. By the end of this article, you will no longer have doubts.

Although essentially different, both Apache Airflow and Apache NiFi are tools designed to manage the golden asset of most organizations: data.

As the data volumes keep expanding, enterprises create a rising need for data warehousing projects and advanced analytics solutions. ETL (Extract, Transform, Load) is a critical component of a modern data stack, as it guarantees that data is successfully integrated across many databases and applications. Both Apache Airflow and Apache NiFi are crème de la crème among the most popular ETL tools. In order to choose the right tool for your needs, you have to ask yourself - what exactly are you going to do with your data? But before that, let’s go through the background and get to know these two pets.


Apache NiFi Basics

NiFi is an abbreviation for Niagara Files, initially produced by the NSA. The platform is written in Java and designed to handle big amounts of data and automate dataflow. It’s a simple, powerful data processing and distribution system, allowing for the creation of scalable directed graphs of data routing and transformation. Data may be filtered, adjusted, joined, divided, enhanced, and verified. Apache NiFi does not require any programming skills, which can be either a benefit or a limitation, and it runs on JVM, supporting JVM languages.

Apache NiFi is an ETL tool typically used for long-running jobs, suitable for processing both periodic batches and streaming data. Data acquisition, transportation, and a guarantee of delivery are all NiFi fortes.

Key Benefits of Apache NiFi

Apache NiFi Limitations

Apache Airflow Basics

Some people claim that Apache Airflow is “cron on steroids”, but to be more precise, Airflow is an open-source ETL tool for planning, generating, and tracking processes. It is compatible with cloud providers such as GCP, Azure, and AWS. Astronomer makes it possible to run Airflow on Kubernetes.

Apache Airflow is a super-flexible task scheduler and data orchestrator suitable for most everyday tasks. Airflow can run ETL/ELT jobs, train machine learning models, track systems, notify, complete database backups, power functions within multiple APIs, and more. Organizations typically use the platform to create workflows as directed acyclic graphs (DAGs) of tasks. Sounds complicated? It really shouldn’t - rich command-line utilities make conducting complex DAG operations a breeze. The Airflow scheduler performs tasks on an array of workers while adhering to specific requirements.

Key Benefits of Apache Airflow

Airflow Limitations


By nature, Apache Airflow is an orchestration framework, not a data processing framework, whereas Apache NiFi’s primary goal is to automate data transfer between two systems. Thus, Airflow is more of a “Workflow Manager” area, and Apache NiFi belongs to the “Stream Processing” category. These two tools aren’t mutually exclusive, and they both offer some exciting capabilities and can help with resolving data silos. It’s a bit like comparing oranges to apples - they are both (tasty!) fruits but can serve very different purposes.

What Apache Airflow and Apache NiFi surely have in common is that they are open-source, community-based tools. Airflow seems to have a broader approval with 23.2K GitHub stars and 9.2k forks, and more contributors. It’s probably due to the fact that it has more applications, as by nature Airflow serves different purposes than NiFi. Still, both tools can offer lots of built-in operators, constant updates, and support from their communities.

Apache NiFi is a perfect tool for handling big data - extracting it and loading it to a given space. It’s an extensible platform known for its great error handling and simple interface. There is no better choice when it comes to the “set it and forget it” kind of pipeline, as NiFi doesn’t offer live monitoring nor statistics per record. It’s perfect if you don’t want to get into coding at all - NiFi is “drag-and-drop” based, and this tool is surely a “go-to” solution for live batch streaming. Scheduling capabilities are not very robust, but technically, NiFi is not a scheduler.

Apache Airflow, on the other hand, is a perfect solution for scheduling specific tasks, setting up dependencies, and managing programmatic workflow. A big, vibrant community keeps updating the tool and making it better with every update. Airflow takes lots of work off your IT team’s hands, because it is one of the most robust platforms for orchestrating workflows or pipelines. It allows you to easily see the dependencies, code, trigger tasks, progress, logs, and success status of your data pipelines. Airflow is a data orchestrator which goes way beyond managing data - it helps to deliver data-driven insights, as a result making businesses grow.

“Before Airflow, our pipelines were split, some things were done on Cron, some on NiFi, some on other tools. We wanted to bring it all together. With NiFi, CRED developers had to make a copy of all the pipelines and use it for their specific use cases. If they wanted to change something, for example, upgrade to a newer Airflow version, they needed to update both copies,“ said Omesh Patil, Data Architect at CRED.

Long story short, there is no “better” tool. It all depends on your exact needs - NiFi is perfect for basic big data ETL process, while Airflow is the “go-to” tool for scheduling and executing complex workflows, as well as business-critical processes.

Astronomer – Making Airflow Even Better

Astronomer enables you to centrally develop, orchestrate, and monitor your data workflows with best-in-class open source technology. With multiple provider packages in our registry, it’s intuitive and straightforward to take off with your ETL pipelines and take full advantage of your data.

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