Cloud Composer (Apache Airflow)

  Quality Thoughts – Best GCP Cloud Engineering Training Institute in Hyderabad

Looking to become a certified GCP Cloud Engineer? Quality Thoughts in Hyderabad is your ideal destination. Our GCP Cloud Engineering course is tailored for graduates, postgraduates, working professionals, and even those from non-technical backgrounds or with educational gaps. We offer a strong foundation in Google Cloud Platform (GCP) through hands-on, real-time learning guided by certified cloud experts.

Our training includes an intensive live internship, focusing on real-world use cases with tools like BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Functions, Dataproc, and IAM. The curriculum covers both fundamentals and advanced GCP concepts including cloud-native app deployment, automation, and infrastructure provisioning.

We prepare you for GCP certifications like Associate Cloud Engineer, Professional Data Engineer, and Cloud Architect, with focused mentorship and flexible learning paths. Whether you're a fresher or a professional from another domain, our personalized approach helps shape your cloud career.

Get access to flexible batch timings, mock interviews, resume building, and placement support. Join roles like Cloud Engineer, Data Engineer, or GCP DevOps Expert after completion.

🔹 Key Features:

  • GCP Fundamentals + Advanced Topics

  • Live Projects & Data Pipelines

  • Internship by Industry Experts

  • Flexible Weekend/Evening Batches

  • Hands-on Labs with GCP Console & SDK

  • Job-Oriented Curriculum with Placement He

Cloud Composer (Apache Airflow)

Cloud Composer is a fully managed workflow orchestration service on Google Cloud, built on Apache Airflow. It allows you to author, schedule, and monitor complex workflows across cloud and on-premises environments using Python-based Directed Acyclic Graphs (DAGs).

With Cloud Composer, you can integrate multiple services like BigQuery, Dataflow, Dataproc, GCS, and external APIs in a single automated pipeline. Workflows are version-controlled and repeatable, making it ideal for ETL, data integration, and ML model deployment.

Key Features:

Fully managed Airflow environment with automatic scaling.

Cross-environment orchestration (multi-cloud & hybrid).

Integration with GCP IAM for secure access control.

Monitoring via Airflow UI and Cloud Logging.

How it works:

Define tasks in Python as Airflow DAGs.

Composer schedules and runs tasks in sequence or parallel.

Logs and metrics are stored in Cloud Monitoring.

Benefits:

Eliminates infrastructure management for Airflow.

Supports dynamic, parameterized workflows.

Scales for large data engineering pipelines.

Best practice: Use environment variables, task retries, and modular DAG design to ensure reliable, maintainable workflows in production.

Read More

Cloud Dataproc (Apache Spark/Hadoop)

Cloud Dataflow (Apache Beam)

BigQuery ML

BigQuery SQL

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