It’s a simple, powerful data processing and distribution system, allowing for the creation of scalable directed graphs of data routing and transformation. The platform is written in Java and designed to handle big amounts of data and automate dataflow. NiFi is an abbreviation for Niagara Files, initially produced by the NSA. Streaming data workflows is not the purpose of this platform. Airflow is not the best choice for stream jobs.Very active, constantly growing open-source community.Airflow offers multiple methods for horizontal scaling. Scalability: It’s easy to define operators and executors, and you can modify the library to meet the amount of abstraction that best suits your context.A powerful Jinja engine for templating makes it possible to parametrize scripts. Due to rich visualization components, you can see all of the running pipelines and follow their progress. It makes it easy to turn schedules on and off, visualize DAG’s progress, make SQL queries, watch pipelines in production, monitor them, and resolve emerging issues at once. Rich UI: The user interface is really intuitive and a truly functional way to access the metadata.Moreover, Python allows for effortless collaboration with data scientists. Customization of complex transformations doesn’t get any simpler than this. Workflows defined as code are easier to test, maintain, and collaborate on. Code-first: Airflow and all the workflows are written in Python (although each step can be written in any language), which allows to dynamically generate DAGs.The Airflow scheduler performs tasks on an array of workers while adhering to specific requirements. Sounds complicated? It really shouldn’t - rich command-line utilities make conducting complex DAG operations a breeze. Organizations typically use the platform to create workflows as directed acyclic graphs (DAGs) of tasks. Airflow can run ETL/ELT jobs, train machine learning models, track systems, notify, complete database backups, power functions within multiple APIs, and more. Astronomer makes it possible to run Airflow on Kubernetes.Īpache Airflow is a super-flexible task scheduler and data orchestrator suitable for most everyday tasks. It is compatible with cloud providers such as GCP, Azure, and AWS. Some people claim that Airflow is “cron on steroids”, but to be more precise, Airflow is an open-source ETL tool for planning, generating, and tracking processes. 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. Both Airflow and NiFi are crème de la crème among the most popular ETL tools. 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. By the end of this article, you will no longer have doubts.Īlthough essentially different, both Apache Airflow and Apache NiFi are tools designed to manage the golden asset of most organizations: data.Īs the data volumes keep expanding, enterprises create a rising need for data warehousing projects and advanced analytics solutions. Still, you may be wondering which one is better suited for your expectations and goals. Start Free TrialĪpache Airflow and Apache NiFi are, in fact, two whistles to a somewhat different tune. Streamline your data pipeline workflow and unleash your productivity, without the hassle of managing Airflow.
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