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Accelerate Migration from Control-M to Apache Airflow with DAGify

This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on using DAGify to convert Control-M export files into a Python Native DAG and upload migrated DAGs in Cloud Composer environment.

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What's inside

Syllabus

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Focuses on converting Control-M export files into Python Native DAGs, which streamlines the migration process to Cloud Composer

Takes place in the Google Cloud console, providing a hands-on environment for practical learning and application

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Reviews summary

Practical control-m to airflow migration lab

According to students, this course provides a highly practical, hands-on lab for accelerating the migration of workflows from Control-M to Apache Airflow using Google Cloud's DAGify tool. Learners found the step-by-step instructions and the direct experience with DAGify to be very useful and effective for understanding the tool's capabilities. While the overall sentiment is largely positive, some students reported encountering minor issues with the lab environment setup or noted that the course assumes prior familiarity with Airflow and GCP. It's considered a valuable, focused resource for professionals undertaking this specific migration task.

Specific to DAGify migration lab only.

"It's very specific to the tool and the Google Cloud environment."

"Don't expect a deep dive into Airflow itself."

"It is clearly aimed at professionals doing this specific migration."

Demonstrates tool's migration capability.

"...using DAGify to migrate Control-M jobs to Airflow..."

"...the process described using DAGify is straightforward."

"The DAGify part was well explained through the steps."

Offers direct experience with migration.

"This lab is excellent! It provides a clear, step-by-step guide..."

"Good practical lab focusing on DAGify. The instructions were mostly clear..."

"Quick and effective lab. It does exactly what it says it will do."

Requires Airflow/GCP console familiarity.

"...felt it assumed a bit too much prior knowledge of GCP console."

"...Assumes familiarity with Airflow concepts."

"Felt a bit lost without strong prior experience in GCP."

Some users had setup or runtime issues.

"...the lab environment seemed a bit fragile. I had issues connecting initially..."

"Had significant issues with the lab environment. Several commands failed..."

"...hit a couple of minor snags that required some troubleshooting..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Accelerate Migration from Control-M to Apache Airflow with DAGify with these activities:

Review Apache Airflow Fundamentals

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Refresh your understanding of Apache Airflow concepts, including DAGs, operators, and tasks, to better grasp the migration process.

Browse courses on Apache Airflow

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Read the official Apache Airflow documentation.
Complete a basic Airflow tutorial.
Review common Airflow operators and their use cases.

Practice Python Scripting

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Strengthen your Python scripting skills, as DAGify generates Python code for Airflow DAGs.

Browse courses on Python

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Work through a Python tutorial focusing on file parsing and manipulation.
Practice writing scripts to read and process data from text files.
Familiarize yourself with Python libraries commonly used in Airflow DAGs, such as 'datetime' and 'os'.

Read 'Programming Apache Airflow'

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Deepen your understanding of Airflow concepts and best practices to improve your ability to migrate workflows effectively.

View Putting Knowledge to Work on Amazon

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Read the chapters related to DAG design and operator usage.
Experiment with the code examples provided in the book.
Consider how the concepts apply to Control-M migration.

Four other activities

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Convert Sample Control-M Definitions

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Practice converting sample Control-M job definitions into Airflow DAGs using DAGify to gain hands-on experience with the migration process.

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Obtain sample Control-M export files.
Use DAGify to convert the files into Airflow DAGs.
Review the generated DAGs and identify areas for optimization.

Document Migration Best Practices

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Create a document outlining best practices for migrating from Control-M to Airflow using DAGify, based on your experience with the lab and additional research.

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Summarize key considerations for a successful migration.
Document common challenges and solutions.
Provide recommendations for optimizing DAGify usage.

Migrate a Real-World Control-M Workflow

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Apply your knowledge by migrating a real-world Control-M workflow to Airflow using DAGify, testing and validating the migrated DAG.

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Identify a suitable Control-M workflow for migration.
Use DAGify to convert the workflow to an Airflow DAG.
Deploy and test the migrated DAG in a Cloud Composer environment.
Document the migration process and any challenges encountered.

Read 'Data Pipelines with Apache Airflow'

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Expand your knowledge of data pipeline design and implementation with Airflow to improve the efficiency and reliability of migrated workflows.

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Read the chapters related to data pipeline architecture and best practices.
Explore the code examples and adapt them to your migration project.
Consider how to optimize the performance of migrated DAGs.

Career center

Learners who complete Accelerate Migration from Control-M to Apache Airflow with DAGify will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data engineers build and maintain the infrastructure that allows for data to be processed and analyzed. This often involves creating and managing data pipelines, which may involve migrating existing systems to new platforms. This course, which focuses on migrating from Control-M to Apache Airflow using DAGify, helps build a practical understanding of the tools and processes involved in this kind of migration. A data engineer may need to convert existing workflows into new formats, and this course provides hands-on experience with that type of conversion. It helps demonstrate a capability to adapt to new environments and technologies. This is a crucial skill for anyone wanting to work as a data engineer.

See salaries and explore the career path for Data Engineer

Cloud Engineer

Cloud engineers architect, implement, and manage cloud-based systems and services. In this role, you might manage the movement of systems from on-premise to the cloud. This course, which provides hands-on experience with migrating workloads from Control-M to Apache Airflow on Google Cloud, may be useful as it demonstrates proficiency with cloud-based migrations. A cloud engineer should be familiar with tools like DAGify, and this course helps build that familiarity. The practical nature of this experience is something that a prospective cloud engineer can point to when applying for jobs.

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Automation Engineer

Automation engineers design, develop, and implement automated systems and processes, often within IT or manufacturing settings. This course provides valuable experience with automating the conversion of Control-M workflows to Apache Airflow through DAGify. An automation engineer must develop and implement complex automated workflows. This course may be useful in developing a solid understanding of how to migrate and automate software and workflow systems. It demonstrates a practical approach to automation challenges, which directly aligns with the responsibilities of an automation engineer.

See salaries and explore the career path for Automation Engineer

Platform Engineer

Platform engineers are responsible for building and maintaining the underlying systems that enable applications and services to run. This course, which focuses on migrating Control-M workflows to Apache Airflow within a cloud environment, helps build a practical understanding of how to manage platform migrations. A platform engineer often works with cloud-based platforms, and this course may be helpful in acquiring that type of experience. By taking this course, a budding platform engineer can get hands-on experience with a real migration task, which is highly relevant to the challenges they will face in this role.

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ETL Developer

ETL developers are responsible for designing, building, and maintaining the processes for extracting, transforming, and loading data. This course, which covers the migration of Control-M workflows to Apache Airflow, helps an ETL developer to understand how to manage such transformations. This course may be useful since ETL developers must deal with migrating data and job workflows on a regular basis. This course may help establish a practical competence needed for anyone wanting to become an ETL developer.

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DevOps Engineer

DevOps engineers are responsible for automating and streamlining the software development lifecycle. This course, which focuses on migrating Control-M to Apache Airflow, helps develop practical experience with a common real-world migration need. Being able to migrate workloads between platforms is helpful for a DevOps engineer as well as a practical demonstration of a candidate's abilities. This course may be useful as it helps build a foundational knowledge of these sorts of migration tasks, which is critical for a DevOps engineer.

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Systems Administrator

Systems administrators manage the day-to-day operations of computer systems and networks. This course, which covers migration of Control-M workflows using DAGify may be useful in helping a systems administrator gain hands-on experience. A systems administrator might be asked to supervise or participate in migrations of systems, services, or workflows. This course provides practical experience with this type of task. It also helps build a real-world understanding of the process, which a prospective systems administrator will find helpful.

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Application Developer

Application developers design, build, and test software applications. While not directly developing applications, this course on migration helps build experience with complex environments. An application developer should be familiar with tools such as Apache Airflow, and this course may be useful as it helps familiarize one with their use. This course, by giving practical experience with workflow migrations, may help provide a wider understanding of the entire software ecosystem for an application developer. A greater understanding of how various services interact with one another is key for the application developer.

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Data Analyst

Data analysts interpret data and use it to make recommendations for business decisions. This course, which focuses on migrating to Apache Airflow, may be useful to demonstrate a practical competence in the management of data workflows. A data analyst should be familiar with tools that are used to manage data pipelines, and this course can help. This course may help a data analyst better understand how the data is processed before it arrives for them to analyze. This helps broaden a data analyst's grasp of the systems behind data.

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Cloud Consultant

Cloud consultants advise organizations on how to best use cloud computing technologies. This course, which focuses on migrating Control-M workflows to Apache Airflow in the cloud, may be useful for a consultant who will advise others on such migrations. The cloud consultant may be called upon to help migrate an organization's infrastructure into the cloud, and this course provides a practical skill that is relevant to that role. For a cloud consultant, having a practical understanding of the types of issues an organization will face can be valuable.

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IT Project Manager

IT project managers oversee the planning, execution, and completion of technology-related projects. This course, on migrating from Control-M to Airflow, may be useful in gaining familiarity with the types of technical challenges involved in migration projects. An IT project manager does not perform hands on work but this course may be useful in helping one understand the effort involved for a technical project such as a system migration. This course can help an IT project manager better understand the practical steps and issues involved in a cloud migration project.

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Database Administrator

Database administrators are responsible for the performance, integrity, and security of databases. This course may be useful as it may help a database administrator get familiarity with how data pipelines are managed. Database administrators don't generally manage migrations, but this course still may be helpful as it gives a view into the processes that deliver data into databases. Understanding these systems can round out a database administrator's understanding of the entire data ecosystem. This can be helpful for troubleshooting issues or for planning future changes.

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Technical Support Specialist

Technical support specialists provide assistance to users of computer systems and software applications. While this role does not directly use the migration techniques taught in the course, learning about system migration from Control-M to Airflow may be helpful. A technical support specialist might be asked to troubleshoot issues related to software migrations. This course may be useful to them because it may help build an understanding of the process. A technical support specialist who understands the migration process can explain issues to users more clearly.

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Business Analyst

Business analysts identify business needs and propose solutions through technology. This course, which covers migrating job workflows using DAGify, may be helpful as it provides insight into a real world data migration process. Although, a business analyst is not responsible for migrations, this kind of experience may be useful as it can help them understand the types of projects that their organization might undertake. This course may be helpful for a business analyst as it can give a broader view of the technology being used.

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Solutions Architect

Solutions architects design complex systems that meet specific business needs. This course, focused on migrating workflows from Control-M to Apache Airflow, helps build a practical grasp of data migration solutions. While not directly involved in hands-on migration, a solutions architect may benefit from an understanding of how such migrations are accomplished. This course helps build a practical knowledge of specific migration challenges. By taking courses like this, a solutions architect can develop more effective and feasible solutions.

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Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Accelerate Migration from Control-M to Apache Airflow with DAGify.

Putting Knowledge to Work

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Provides a comprehensive guide to Apache Airflow, covering everything from basic concepts to advanced features. It's a valuable resource for understanding how Airflow works and how to use it effectively. This book is commonly used by industry professionals. It adds depth to the course by providing real-world examples and best practices.

Putting Knowledge to Work

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Putting Knowledge to Work

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