Google Cloud composer:

Google Cloud composer:

Google Cloud Composer is a solution for those who need to run many workflows at once and find it difficult to manage so much work at once. It is a popular open-source tool that is built on Apache Airflow. It is used to author, schedule, and monitor distributed workflows. It helps organizations to orchestrate their batch data workflows/pipelines. We can set up workflows to run automatically or manually using Google Cloud Composer. We can also track workflow execution in real-time.They bases the pricing strategy on the customers’ usage. As a result, they will not charge you for services that you do not use.

 To set up the cloud composer, first, you need to enable the cloud composer API. Then, after clicking the “create” button, you can create a new environment. Next, you are asked to fill in all the details of the environment. These details comprise the name and location of the environment. You also need to mention disc size, machine type, version of python and image version, etc. After some time, this setup process will be completed. And a green checkmark will show the completion of the process.

The architecture of Cloud Composer:

Cloud Storage, App Engine, Airflow DAGs, and Cloud SQL are the core pillars of Cloud Composer architecture. The Airflow DAG plugin and related logs are all stored in Cloud Storage. Submission of DAGs and code takes place here. Airflow metadata is stored in Cloud SQL, which is backed up daily. The Airflow web server is hosted by the app engine, and the Cloud Composer IAM policy enables you to manage access to it. Thus, Airflow DAGs are nothing but a collection of tasks, often known as workflows.

Apache Airflow’s Components:

Apache Airflow is a workflow engine that allows Python scripts to be used to create data pipelines. The user interface, or GUI, of Apache Airflow, is the web-server. This is used to keep track of job status. The Scheduler component is to coordinate and scheduling of tasks. The executor is a cluster of worker processes that are used to conduct the workflow’s tasks. Metadata database contains metadata for DAGs, jobs, and other items.

DAGs:

Knowing Directed acyclic graphs (DAGs) helps us understand how Cloud composer works. DAGs are used to create workflows in the Airflow. DAGs help you collect the tasks that you wish to schedule and run. They organize the tasks to showcase their relationships and dependencies. DAG Executes the task not only at the right time but also in the right order and with the right issue handling. Each task in DAG can perform multiple functions- like performing data ingestion, emailing, and running a pipeline.

Further, DAGs can be stored in a cloud storage bucket with the help of a cloud composer. It allows you to easily create, alter, and delete the DAG. When you build an environment, it will create a Cloud Storage bucket. You can use either manual or automatic deployment to set up Google Cloud Composer. Drag and drop the Python files (with a.py extension) to the DAGs folder in Cloud Storage if you’re intending to manually deploy DAGs. Alternatively, you can put up a continuous integration pipeline to automatically deploy DAG files.

Features: 

First, Google cloud composer is known for its simplicity. It has a user-friendly interface, hence setting up the environment is no big deal. With a google cloud account, you can easily set up a pre-configured new Airflow. Thus, it saves your time to build and configure the required infrastructure. Since it is a managed service, you don’t need to worry about the maintenance of infrastructure. Dedicated DevOps, I.e. Google Cloud, will do the job for you. It handles technical complexity so that you can make the best use of airflow without worrying about installation, management and backup overhead, etc. second, by modifying the underlying architecture, Google Cloud Composer projects can be ported to any other platform. Third, the feature of hybrid cloud operations combines cloud scalability with on-premise data center security. And last, Because Apache Airflow is written in Python (a programming language for big data and machine learning.), you can quickly create, troubleshoot, and maintain it. In addition, the safety aspects of Cloud Composer make the compute node publicly inaccessible since it uses private IP to protect it from the public internet. This is for clients who have been authenticated to use the Airflow user interface.

Conclusion:

Thus, Cloud Composer not only helps you manage and choreograph your data pipelines, but it also works effectively with several other Google products via well-defined APIs. While not having to configure the infrastructure yourself is a benefit of adopting a managed service like Google Cloud Composer, it also means paying extra for a ready-made managed service. In addition, Cloud Composer only supports a few services and integrations. To debug DAG connectors, you’ll need a deeper understanding of the Google Cloud Platform.

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