Course Overview of Build Batch Data Pipelines on Google Cloud:

This instructor-led course focuses on designing and implementing robust batch data pipelines on Google Cloud. Participants will explore large-scale data ingestion, transformation, and workflow orchestration using modern tools such as Dataflow and Serverless for Apache Spark.

Through hands-on labs and real-world scenarios, learners will gain practical experience in ensuring data quality, optimizing performance, and implementing monitoring and alerting mechanisms for reliable batch processing systems.

After completing Build Batch Data Pipelines on Google Cloud, participants will be able to:

  • Identify when to use batch data pipelines for business use cases
  • Design scalable pipelines for large-scale data processing
  • Implement data ingestion and transformation workflows
  • Use Dataflow and Dataproc Serverless for pipeline execution
  • Apply data quality validation and cleansing techniques
  • Handle schema evolution and data deduplication
  • Orchestrate workflows using Cloud Composer
  • Monitor pipelines using logging, alerts, and observability tools

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Key Features of Build Batch Data Pipelines on Google Cloud:

  • 4 Learning Modules focused on batch data engineering

  • 4 Hands-On Labs using real Google Cloud tools

  • Implementation using Dataflow and Serverless Spark

  • Data Quality and Validation Techniques

  • Workflow Orchestration using Cloud Composer

  • Monitoring and Observability Best Practices

Who should Attend Build Batch Data Pipelines on Google Cloud ?

  • Data Engineers
  • Data Analysts
  • ETL Developers
  • Cloud Data Engineers
  • Analytics Engineers
  • Big Data Professionals
  • Developers working with large-scale data processing systems
  • Professionals interested in Google Cloud Data Engineering solutions

Prerequisites of Build Batch Data Pipelines on Google Cloud:

  • Basic understanding of data warehousing and ETL/ELT concepts
  • Basic knowledge of SQL
  • Familiarity with Python (recommended)
  • Understanding of Google Cloud fundamentals

    • Why choose CloudThat as your training partner for Build Batch Data Pipelines on Google Cloud?

    • Specialized Google Cloud Data Engineering Expertise  -CloudThat specializes in cloud, data, and analytics technologies, delivering industry-focused Google Cloud training programs with practical implementation experience and enterprise use cases. 
    • Industry-Recognized Trainers - Our trainers are certified Google Cloud professionals with expertise in Data Engineering, BigQuery, Dataflow, Dataproc, and enterprise-scale analytics solutions. 
    • Hands-On Learning Approach - CloudThat emphasizes practical learning through guided labs, demos, troubleshooting exercises, and real-world data engineering implementation scenarios. 
    • Customized Learning Paths - Training paths are designed for data engineers, analysts, developers, and cloud professionals with varying levels of expertise and business requirements. 
    •  Interactive and Practical Sessions - Training includes architecture discussions, implementation walkthroughs, pipeline debugging, optimization exercises, and collaborative learning activities.
    •  Career and Certification Support - CloudThat supports learners with project guidance, interview preparation, and career-focused learning paths for Google Cloud data engineering roles. 
    • Updated Industry-Relevant Content - Course content is continuously updated to align with the latest advancements in Google Cloud data engineering, serverless analytics, and enterprise data processing technologies. 
    • Trusted by Enterprises Worldwide - Thousands of professionals and organizations trust CloudThat for advanced cloud, data engineering, and analytics training programs. 

    Learning Objective of Course

    •  Understand batch processing concepts and enterprise use cases 
    •  Design scalable and reliable batch data processing architectures
    • Implement data ingestion and transformation pipelines on Google Cloud
    • Build and optimize pipelines using Dataflow and Dataproc Serverless
    • Apply data quality validation and cleansing techniques
    • Handle schema evolution and deduplication workflows
    • Implement orchestration using Cloud Composer
    • Monitor and troubleshoot enterprise batch pipelines
    • Utilize Cloud Data Fusion for pipeline visualization and integration
    • Apply operational and performance optimization best practices for large-scale data systems 

    Course Outline of Build Batch Data Pipelines on Google Cloud: Download Course Outline

    Lecture Content

    • Introduction to Batch Data Pipelines
    • Use Cases and Business Scenarios
    • Processing Challenges in Batch Systems
    • Role of a Data Engineer

    Lab Content

    • NA

    Lecture Content

    • Designing Scalable Batch Pipelines
    • Large-scale Data Transformations
    • Dataflow and Serverless for Apache Spark
    • Data Ingestion and Orchestration
    • Performance Optimization Techniques

    Lab Content

    • Lab: Build Batch Pipeline using Serverless for Apache Spark
    • Lab: Build Batch Pipeline using Dataflow

    Lecture Content

    • Data Validation and Cleansing
    • Error Logging and Analysis
    • Schema Evolution Strategies
    • Data Deduplication Techniques

    Lab Content

    • Lab: Data Quality Validation using Serverless Spark

    Lecture Content

    • Workflow Orchestration Concepts
    • Cloud Composer for Scheduling
    • Monitoring and Observability
    • Alerts and Troubleshooting
    • Pipeline Visualization

    Lab Content

    • Lab: Building Pipelines using Cloud Data Fusion

    Certification Details of Build Batch Data Pipelines on Google Cloud:

      Course Completion Certificate

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    Course ID: 28405

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    FAQs for Build Batch Data Pipelines on Google Cloud:

    Data engineers and data analysts working with large-scale data processing.

    Batch pipelines, Dataflow, Dataproc Serverless, data quality, orchestration, and monitoring.

    Basic knowledge of SQL and Python is recommended.

    1 day (approximately 480 minutes).

    Yes, multiple labs using real-world scenarios.

    Yes, using Cloud Composer and Data Fusion.

    Yes, including logging, alerts, and observability.

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