Google Cloud Professional Data Engineer Course [2019 Update]

Cloud Composer, also known as Apache Airflow on GCP, is a fully managed workflow automation and scheduling platform for executing, tracking, and monitoring datasets and machine learning pipelines. It provides a scalable infrastructure that can handle enterprise-level workload management and orchestrates complex workflows by coordinating different cloud services and resources.

Here's a breakdown of what Cloud Composer offers:

1. Centralized Workflow Management: Cloud Composer allows you to design, schedule, and execute data engineering workflogs with the help of a visual interface and user-friendly programming constructs.

2. Scalability: You can scale your workflows as per the needs of your project without worrying about managing or provisioning resources.

3. Extensibility: Cloud Composer is built on Apache Airflow, which means it supports custom extensions and integrates seamlessly with other GCP services and products.

4. Monitoring and Logging: It integrates with Stackdriver (now called Google Cloud Monitoring) to monitor workflows and logs for debugging and tracking performance issues.

5. Compliance and Security: Cloud Composer ensures that your data remains secure by encrypting the data at rest and in transit, and by managing sensitive credentials via IAM roles.

6. Ease of Use: With a user-friendly interface, Cloud Composer simplifies the process of orchestrating complex workflows involving multiple steps and dependencies.

7. Fully Managed Service: Google manages all aspects of running Apache Airflow as a fully managed service on Kubernetes Engine, so you can focus on writing your workflows rather than managing infrastructure.

8. Integration with BigQuery and Dataflow: It provides pre-built integrations for Google Cloud services such as BigQuery and Cloud Dataflow, making it easier to ingest, process, and analyze data within your orchestrated workflows.

9. Customizability: Users can define custom operations or use community-contributed plugins to extend Airflow's capabilities.

10. Collaboration: Team members can collaborate on building pipelines through shared environments and version control integration with Git and Cloud Source Repositories.

In the context of the Google Cloud Data Engineer certification, understanding Cloud Composer is essential as it may be used to demonstrate knowledge of orchestrating workflows, integrating with other GCP services, managing data pipelines, and ensuring scalability and reliability. It's a powerful tool for automating complex processes in the cloud, and mastering it can significantly enhance your data engineering skills on GCP.