Home Projects Architecture Case Studies GCP
Coming Soon GCP GCP Data Engineer

Serverless Data Pipeline with Dataflow

PRJ-GCP-DATA-076

Scalable ETL pipeline with Apache Beam

~8 min read Intermediate
Status Coming Soon
Last Updated Jan 16, 2026
Completion 0%
Status: Coming Soon· Last Updated: Jan 16, 2026· Completion: 0%· ~8 min read· Intermediate
Download Guide Watch Tutorial View Architecture Download Architecture

Implementation Guide

Comprehensive step-by-step deployment guide

Download Implementation Guide

Estimated Monthly Cost

~$42/mo on minimal config
ComputeStorageMonitoring
Business ContextTraditional ETL processes struggle with scaling to handle fluctuating data volum…

The Problem

  • Traditional ETL processes struggle with scaling to handle fluctuating data volumes from diverse sources, leading to bottlenecks and delayed insights.
  • Managing and maintaining on-premise or VM-based data processing infrastructure is resource-intensive, requiring significant operational overhead and specialized expertise.
  • Lack of real-time data processing capabilities hinders immediate decision-making and responsiveness to critical business events.

The Solution

  • Implements a fully managed, serverless data pipeline using GCP Dataflow for scalable and efficient data transformation.
  • Leverages Apache Beam for unified batch and stream data processing, ensuring consistency and flexibility across various data sources.
  • Utilizes GCP Pub/Sub for real-time ingestion of streaming data, enabling immediate processing and analysis.

Business Value

  • Reduces data processing latency by 70%, enabling near real-time analytics for critical business operations.
  • Achieves a 40% reduction in operational costs by eliminating infrastructure provisioning and management overhead.
  • Increases data processing throughput by 5x during peak loads without manual intervention, ensuring business continuity.
  • Improves data quality and consistency by 25% through unified processing logic across batch and streaming data.

Risk Mitigation

  • Addresses scalability risks by using Dataflow's auto-scaling capabilities, preventing performance degradation under high load.
  • Mitigates operational overhead risks through serverless architecture, reducing the need for manual infrastructure management.
  • Reduces data loss risks with Pub/Sub's at-least-once delivery guarantee and Dataflow's fault-tolerant processing.
GRC MappingISO 27001:2022(A.8.1.1 - Inventory of Information and Other Associated Assets): …

Compliance Frameworks

  • ISO 27001:2022 (A.8.1.1 - Inventory of Information and Other Associated Assets): Ensures all data assets processed by the pipeline are identified and managed.
  • NIST SP 800-53 Rev. 5 (RA-3 - Risk Assessment): Incorporates continuous risk assessment for data processing activities within Dataflow.
  • GDPR (Article 25 - Data protection by design and by default): Implements privacy-by-design principles in data handling and processing.
  • SOC 2 Type 2 (Common Criteria 6.1 - Logical and Physical Access Controls): Demonstrates controls over access to data and systems within the pipeline.

Security Controls Implemented

  • Dataflow Job Access Control: Implements IAM roles and permissions to restrict access to Dataflow jobs and their underlying resources.
  • Pub/Sub Message Encryption: Ensures data at rest and in transit within Pub/Sub is encrypted using Google-managed or customer-managed encryption keys.
  • BigQuery Column-level Security: Applies fine-grained access control to sensitive data within BigQuery datasets, restricting access to authorized users.
  • VPC Service Controls: Establishes security perimeters around Dataflow, Pub/Sub, and BigQuery to prevent data exfiltration.
  • Cloud Audit Logs: Captures detailed audit trails of all administrative activities and data access events across the tech stack.

Audit Evidence

  • Dataflow Job Execution Logs: Detailed records of pipeline runs, including start/end times, status, and resource utilization.
  • BigQuery Audit Logs: Logs of all queries, data manipulations, and access attempts on BigQuery datasets.
  • IAM Policy Bindings: Documentation of all Identity and Access Management policies applied to Dataflow, Pub/Sub, and BigQuery resources.
  • Dataflow Pipeline Definition Files: Version-controlled Apache Beam code defining the ETL logic and data transformations.

Regulatory Alignment

  • GDPR (Article 32 - Security of processing): Ensures appropriate technical and organizational measures for data security in the pipeline.
  • CCPA (Section 1798.100 - Consumer's Right to Know): Supports data subject access requests by enabling efficient data retrieval from BigQuery.
  • HIPAA (45 CFR Part 164.312 - Technical Safeguards): Implements access controls and audit mechanisms for Protected Health Information (PHI) processed.
  • PCI DSS (Requirement 3 - Protect Stored Cardholder Data): Ensures encryption and protection of any cardholder data handled by the pipeline.

Video tutorial coming soon!

Subscribe to our YouTube channel to get notified when this tutorial is published.

Subscribe on YouTube

Architecture Diagram

PRJ-GCP-DATA-076 Architecture

Technology Stack

Dataflow
Apache Beam
Pub/Sub
BigQuery
ETL

Complete Documentation

Prerequisites

Project Owner or Editor role
gcloud CLI configured
Terraform >= 1.5 (optional)
GCP project with billing enabled
Service Account with required APIs
1

Clone & Authenticate

Clone the repository and authenticate with gcloud using your service account key or application default credentials.

gcloud auth application-default login
2

Enable Required APIs

Enable all required GCP APIs for this project in your target project.

gcloud services enable compute.googleapis.com container.googleapis.com
3

Initialize Infrastructure

Run Terraform init and plan to preview the GCP resource changes before applying.

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all GCP resources in your target project.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the GCP Console and check Cloud Monitoring for any errors.

gcloud logging read "severity>=ERROR" --limit 50

Deployment Guide

Step-by-step instructions to deploy this project

Download Guide

Architecture Diagram

Visual representation of the system architecture

Download Architecture

Source Code

Complete source code and configuration files

View on GitHub

Video Tutorial

Watch the complete walkthrough video

Watch Now