![]() Although Airflow is not perfect, the community is working on a lot of critical features that are crucial to improving the performance and stability of the Airflow platform. Built by numerous Data Engineers, Airflow is a complete solution and solves countless Data Engineering Use Cases. Since its inception, several functionalities have already been added to Airflow. The ability to add custom hooks/operators and other plugins helps users implement custom use cases easily and not rely on Airflow Operators completely. Extensibility and Functionality: Apache Airflow is highly extensible, which allows it to fit any custom use cases. ![]() We have more than 1000 contributors contributing to Airflow, and the number is growing at a healthy pace. The Airflow Community has been growing ever since. Community: Airflow was started back in 2015 by Airbnb.Here are a few reasons why Airflow wins over other platforms: Why You Should Use Apache Airflow for ETL/ELT Airflow Pipeline Organizations are increasingly adopting Airflow to orchestrate their ETL/ELT jobs. Airflow is natively integrated to work with big data systems such as Hive, Presto, and Spark, making it an ideal framework to orchestrate jobs running on any of these engines. ![]() Here’s an example of a Dag that generates visualizations from previous days’ sales.Įfficient, cost-effective, and well-orchestrated data pipelines help data scientists develop better-tuned and more accurate ML models because those models have been trained with complete data sets and not just small samples. Similarly, to create your visualizations it may be possible that you need to load data from multiple sources. The analogy also shows that certain steps like kneading the dough and preparing the sauce can be performed in parallel as they are not interdependent. Similarly, to create your visualization from the past day’s sales, you need to move your data from relational databases to a data warehouse. Similarly, for Pizza sauce, you need its ingredients. Like, to knead the dough, you need flour, oil, yeast, and water. Now, the DAG shows how each step is dependent on several other steps that need to be performed first. Workflows usually have an end goal like creating visualizations for sales numbers of the last day. ![]() Let’s use a pizza-making example to understand what a workflow/DAG is. In Airflow, these workflows are represented as Directed Acyclic Graphs (DAG). These data pipelines deliver data sets that are ready for consumption either by business intelligence applications and data science or machine learning models that support big data applications. Orchestration of data pipelines refers to the sequencing, coordination, scheduling, and managing of complex data pipelines from diverse sources. Apache Airflow is used for the scheduling and orchestration of data pipelines or workflows. ![]()
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