12/29/2023 0 Comments Airflow emrWhile managing workflows on AWS, the Airflow system uses Amazon EMR and Genie as open-source technologies. Therefore, using Airflow on AWS, centralized platform teams can maintain their big data platform, service various concurrent ETL workflows, and simplify operational tasks needed to attain the process. With the continuous urge to extend and update the big data platform as a way of keeping up with the latest big data processing frameworks, what is required is an efficient architecture that simplifies big data platform management and enhances easy access to the big data applications. Large companies that run big data ETL workflows on the AWS work at a scale where most internal end-users are serviced and several concurrent pipelines are also serviced. In a different discussion on the practical use of Airflow to create on-demand or scheduled workflows that process complicated data from different data providers, Oliveira and Raditchkov (2019) argue that it becomes easier to orchestrate big data workflows with Apache Airflow. With Airflow’s operators, integration with various data systems like cloud storage, databases, and data warehouses is made easier. This enables data engineers to transform, extract, and load data from different sources. Hence, in relation to using Airflow for managing workflows, Airflow provides a reliable and scalable framework for users to orchestrate data workflows. Users who run Airflow on AWS should consider Amazon Managed Workflows for Apache Airflow since it helps to set up Airflow, provisioning and autoscaling capacity (storage and compute), keeps Airflow up-to-date, and automates snapshots. Alvarez-Parmar and Maisagoni (2021) note that “Managed Workflows is a managed orchestration service for Apache Airflow that makes it easy for data engineers and data scientists to execute data processing workflows on AWS” Airflow helps users orchestrate workflows as well as manage how they are executed without configuring, managing, and scaling the Airflow architecture. Therefore, one practical situation requiring a user to run Airflow is when the user requires managing workflows. Similarly, one does not need to guess the capacity of the server to run the Airflow cluster or worry about autoscaling groups and bin packing to maximize resource utilization. In relation to managing workforces, with AWS Fargate, a user can run the core components of Airflow without creating and managing servers. Airflow operates more than a batch processing platform since it allows the user to develop pipelines to process data and run different complex jobs in a distributed and complex manner. Airflow runs on AWS Fargate alongside Amazon Elastic Container Service (ECS) as an orchestrator, implying that the user does not have to provision as well as manage servers. While using Airflow, one is able to orchestrate and automate complex data pipelines. Alvarez-Parmar and Maisagoni (2021) argue that as an open-source distributed workflow management platform, Airflow allows users to schedule, orchestrate, as well as monitor workflows. One scenario that demands the use of Apache Airflow is managing workflows. Open Airflow is used on AWS in different scenarios, as described in this context. DAG constitutes the tasks a user whatnot to run, and they are organized in ways that reflect their dependencies and relations hence a task is a unit of work within the DAG (Grzemski, 2020). Scheduling is a process of controlling, planning, and optimizing when tasks need to be done while authoring workflows using Airflow means writing Python scripts to generate Directed acyclic graphs (DAGs). In the realm of the major operation of Airflow, workflow constitutes a sequence of tasks that process data to aid the user in building pipelines. As a platform developed to schedule, author, and monitor workflows programmatically, Airflow provides different features that help define, schedule, create, monitor, and execute data workflow (Grzemski, 2020). As it was created in 2014 by Airbnb, the aim was to help manage increasingly complex workflows at the company hence remained open-sourced from the start. Airbnb initially developed Apache Airflow as an open-source platform to programmatically schedule, author, and monitor data pipelines and workflows.
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