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Orchestrating a Multi-Tool Batch Pipeline: Integrating Cloud Storage, Dataproc, and BigQuery with Cloud Composer (Apache Airflow)

Pipeline

Integrating Cloud Storage, Dataproc, and BigQuery with Cloud Composer (Apache Airflow)

  • Use Case : Organizations dealing with large-scale batch processing workflows (ETL pipelines, data cleansing, transformations, aggregations) need a way to orchestrate multiple GCP services seamlessly. This use case enables end-to-end scheduling, dependency management, and monitoring of workflows across Cloud Storage, Dataproc (Spark/Hadoop), and BigQuery using Cloud Composer (Airflow).

Objective

  • To design and implement an automated batch pipeline where data lands in Cloud Storage (raw zone).

    Transformation and processing are executed via Dataproc clusters.

    Results are loaded into BigQuery for analytics.

    Orchestration, scheduling, and retries are handled by Cloud Composer (Airflow DAGs).

    Ensure fault tolerance, automation, and scalability for enterprise-scale batch pipelines.

Project Description

  • Data Ingestion :

    Raw data (CSV, JSON, Parquet, logs, etc.) is ingested into Cloud Storage (raw bucket).

    Workflow Orchestration with Cloud Composer:

    An Airflow DAG is defined in Cloud Composer to orchestrate steps.

    Tasks include triggering Dataproc jobs, monitoring execution, and loading results into BigQuery.

Batch Processing with Dataproc

  • Spark/Hadoop jobs on Dataproc clean, enrich, and transform the ingested data.

    Processed data is written back to Cloud Storage in Parquet/optimized formats.

Analytics with BigQuery

  • Transformed data is loaded into BigQuery tables.

    Supports SQL analytics, dashboarding, and reporting.

Automation & Monitoring

  • Cloud Composer handles scheduling, retries, and notifications on job failures.
  • Cloud Monitoring & Logging captures performance metrics.
  • Google Cloud Services & Technologies :
    Service/Technology Role
    Cloud Storage Raw data ingestion + staging + processed data storage.
    Dataproc (Spark/Hadoop) Distributed batch processing (ETL transformations, aggregations).
    BigQuery Data warehouse for analytics and visualization.
    Cloud Composer (Apache Airflow) Workflow orchestration, scheduling, dependency management.
    Pub/Sub (optional) Event-driven triggers for batch pipeline runs.
    Cloud Monitoring & Logging Tracking pipeline performance, error alerts, and logs.
    IAM & Cloud KMS Secure access control + data encryption.
    Looker Studio / BI Engine Business intelligence dashboards and query acceleration.