Overview of Google Cloud Data Flow

Apr 5, 2025

Tech Capture Lecture Notes: Google Cloud Data Flow

Introduction

  • Previous video covered Google Cloud data processing services: Data Flow, Data Fusion, Dataproc, Cloud Composer.
  • This lecture focuses on Google Cloud Data Flow in detail.

Google Cloud Data Flow

Overview

  • Unified stream and batch data processing service based on Apache Beam.
  • Stream Processing: Real-time processing of data as it arrives (e.g., banking transactions).
  • Batch Processing: Processing data at scheduled intervals (e.g., end-of-day transaction processing).

Significance

  • Supports both streaming and batch processing.
  • Ideal for applications needing low latency insights.
    • Streaming: Immediate data availability for analytics.
    • Batch: Data available after the processing window.

Use Cases

  • Real-time stream processing for IoT devices and logs.
  • Large-scale data transformation.
  • Building real-time dashboards and analytics.

Creating a Data Flow Job

Options for Creating Jobs

  1. Data Flow Template
    • Reusable pipelines.
    • Custom templates or Google-provided templates.
  2. Data Flow Job Builder
    • No-code UI for building and running data flow pipelines.

Demonstration: Creating a Data Flow Job

  • Example: Load 1,000 records from Google Cloud Storage (GCS) to BigQuery.

Steps to Create a Data Flow Job

  1. Using Google Cloud Console
    • Create a new project.
    • Navigate to Data Flow.
  2. Job Creation Options
    • Template: Choose pre-built templates for common scenarios.
    • Job Builder: Use Google's new UI for job creation.
  3. Setup
    • Define input source (CSV file on GCS) and output (BigQuery schema).
    • Create a bucket for storage and upload the CSV file.
    • Define the JSON schema for BigQuery.
    • Use JavaScript UDF for data transformation.
  4. Execution
    • Enable necessary APIs and permissions in Google Cloud.
    • Start the job and monitor worker nodes (VM instances).
    • Handle errors by checking logs and revising inputs.

Troubleshooting

  • Common errors include permissions issues and incorrect function names.
  • Ensure correct file uploads and formats (e.g., JSON, JavaScript functions).

Conclusion

  • Successfully created a data pipeline using Data Flow.
  • Transformed and loaded data from GCS to BigQuery.
  • Example highlighted transforming CSV data to JSON format for data flow jobs.
  • Future videos to explore more on Job Builder and other advanced topics.