Agile and cloud-native solutions are in high demand in the quickly developing fields of workflow orchestration and data engineering. Control-M and other legacy enterprise schedulers have long served as the backbone of many organizations’ operations. However, Apache Airflow has become the go-to option for contemporary data workflow management as the market moves towards more adaptable and scalable systems. However, switching from Control-M to Apache Airflow can be difficult and time-consuming.
In many different industries, Control-M has shown to be a dependable and strong solution for handling batch processes and workflows. However, its proprietary nature and constraints may make it difficult for businesses to adopt more agile development methods and cloud-native designs. With its robust orchestration features, large community support, and open-source architecture, Apache Airflow presents a strong substitute. However, switching from Control-M, a system with a strong foundation, to Airflow is no easy task. Converting complex work descriptions, dependencies, and timelines is part of the process, which frequently calls for a lot of manual labor and skill.
In a recent research, a team of researchers from Google introduced DAGify, an open-source program that streamlines and expedites this transition from Control-M to Airflow. DAGify offers an automated conversion solution to help overcome this difficulty. It helps businesses to convert their current Control-M task definitions into Directed Acyclic Graphs (DAGs) in Airflow, which minimizes the chance of errors during the migration and lessens the manual labor required.
Teams can concentrate on streamlining their workflows in Airflow instead of getting bogged down in the difficulties of manual conversion when they use DAGify to ease the migration process. Fundamentally, DAGify uses a template-driven method to make it easier to convert Control-M XML files into the native DAG format of Airflow. This technique makes DAGify extremely flexible in different Control-M configurations and Airflow requirements. The program extracts vital data about jobs, dependencies, and schedules by parsing Control-M XML files. After that, the data is mapped to the tasks, dependencies, and operators in Airflow, maintaining the fundamental framework of the initial workflow.
DAGify is highly configurable due to its template system, which lets users specify how Control-M properties should be converted into Airflow parameters. An Airflow SSHOperator, for instance, can have a Control-M “Command” task mapped to it via a user-defined YAML template. In order to ensure a smooth transition from Control-M to Airflow, this template outlines how attributes like JOBNAME and CMDLINE are included in the created DAG.
DAGify comes with a number of pre-made templates for typical Control-M job kinds. Users can alter these templates to suit their own requirements. Because of its adaptability, the tool can support a large variety of Control-M settings, ensuring a seamless migration procedure.
Google Cloud Composer is a compelling choice for enterprises using a fully managed Airflow solution. By simplifying the management of Airflow infrastructure, Cloud Composer frees teams up to concentrate on creating and coordinating their data pipelines. The migration of Control-M workflows to a cloud-native environment is now simpler than ever because of DAGify’s seamless integration with Google Cloud Composer. Through this integration, the migration process can be made even more efficient and scalable, allowing organizations to reap the benefits of Airflow in the cloud more rapidly.
In conclusion, DAGify is a big step forward in making the switch from Control-M to Apache Airflow easier. Organizations can move to Airflow more quickly and confidently using DAGify’s automated conversion process and easy integration with Google Cloud Composer. DAGify is a priceless tool that can help speed up the transition and realize the full potential of Apache Airflow in data engineering operations, regardless of the user’s level of experience with the platform.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.
