Agile, cloud-native solutions are in high demand in the rapidly evolving 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 emerged as the preferred choice for contemporary data workflow management as the market moves toward more adaptable and scalable systems. However, switching from Control-M to Apache Airflow can be difficult and time-consuming.
Across many different industries, Control-M has proven to be a reliable and robust solution for managing batch processes and workflows. However, its proprietary nature and limitations can make it difficult for companies to adopt more agile development methods and cloud-native designs. With its robust orchestration features, strong community support, and open-source architecture, Apache Airflow presents a solid substitute. However, switching from Control-M, a system with a solid foundation, to Airflow is no easy task. Converting complex job descriptions, dependencies, and schedules is part of the process, which often requires a lot of manual work and skill.
In a recent research, a team of Google researchers introduced DAGify, an open source program that simplifies and accelerates this transition from Control-M to Airflow. DAGify offers an automated conversion solution to help overcome this difficulty. It helps companies convert their current Control-M task definitions into directed acyclic graphs (DAGs) in Airflow, minimizing the possibility of errors during migration and reducing the manual work required.
Teams can focus on optimizing their workflows in Airflow instead of getting bogged down in the difficulties of manual conversion when they use DAGify to ease the migration process. Basically, DAGify uses a template-based method to facilitate the conversion of Control-M XML files into Airflow’s native DAG format. This technique makes DAGify extremely flexible across 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 tasks, dependencies, and operators in Airflow, maintaining the fundamental framework of the initial workflow.
DAGify is highly configurable thanks to its template system, which allows users to specify how Control-M properties should be translated into Airflow parameters. For example, an Airflow SSHOperator can have a Control-M “command” task assigned to it using a user-defined YAML template. To ensure a smooth transition from Control-M to Airflow, this template describes how attributes such as JOBNAME and CMDLINE are included in the created DAG.
DAGify includes a number of predefined templates for typical Control-M job types. Users can modify these templates to suit their own requirements. Thanks to its adaptability, the tool can support a wide variety of Control-M configurations, ensuring a smooth migration process.
Google Cloud Composer is an attractive option for companies using a fully managed Airflow solution. By simplifying the management of Airflow infrastructure, Cloud Composer frees up teams to focus on building and coordinating their data pipelines. Migrating Control-M workflows to a cloud-native environment is now easier than ever thanks to DAGify’s seamless integration with Google Cloud Composer. Through this integration, the migration process can become even more efficient and scalable, allowing organizations to take advantage of the benefits of Airflow in the cloud more quickly.
In conclusion, DAGify is a major step forward in easing the transition from Control-M to Apache Airflow. Organizations can migrate to Airflow more quickly and confidently through DAGify’s automated conversion process and easy integration with Google Cloud Composer. DAGify is an invaluable tool that can help accelerate the transition and leverage 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 student of the University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking skills, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
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