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Resource-Aware Workflow Optimization for Big Data Jobs Using Cloud Composer and Dataflow

Cloud Composer

Big Data Jobs Using Cloud Composer and Dataflow

  • Use Case : Organizations processing large-scale datasets (e.g., logs, transaction data, sensor streams) often face high costs and inefficient resource utilization in cloud pipelines. Optimizing workflow execution, resource allocation, and scheduling ensures faster job completion and lower costs.

Objective

  • Optimize big data workflows in Google Cloud to efficiently allocate compute resources.

    Minimize execution time, cost, and idle resources for Dataflow jobs.

    Automate scheduling, dependencies, and monitoring using Cloud Composer (managed Apache Airflow).

    Enable scalable, fault-tolerant, and resource-aware ETL pipelines for analytics and ML workloads.

Project Description

  • This project implements a resource-aware big data workflow optimization system:

    Workflow Definition: Use Cloud Composer to define DAGs (Directed Acyclic Graphs) for ETL, batch, or streaming pipelines.

    Data Processing: Use Dataflow to process large-scale data efficiently with dynamic resource allocation.

    Resource-Aware Optimization: Apply heuristics or ML-based strategies to adjust worker nodes, memory, and CPU allocation per job.

    Monitoring & Feedback: Use Cloud Monitoring to track job latency, throughput, and resource utilization, then update workflow policies.

    Automation: Cloud Composer automates retries, dependencies, and scheduling based on resource requirements and SLAs.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud Composer Orchestrates ETL and big data workflows; manages scheduling, retries, and dependencies.
    Dataflow Processes large-scale batch or streaming datasets; supports autoscaling and dynamic resource allocation.
    Cloud Storage Stores raw, intermediate, and processed datasets for the workflows.
    BigQuery Stores structured processed data for analytics and reporting.
    Cloud Monitoring / Logging Tracks job performance, resource utilization, and workflow health.
    Cloud Functions Triggers auxiliary workflows, notifications, or automated responses based on job events.
    Pub/Sub Streams real-time data into Dataflow pipelines for processing.
    Vertex AI (Optional) Can be used to predict optimal resource allocation for workflows using historical performance data.
    Cloud Key Management Service (KMS) Encrypts sensitive data in pipelines for security and compliance.
    Looker / Data Studio Visualizes workflow performance, resource usage, and processed data insights.