Course Overveiw of Introduction to Data Engineering on Google Cloud

This instructor-led course introduces learners to data engineering concepts on Google Cloud and explains how data engineering responsibilities map to Google Cloud services and solutions. Participants will explore data engineering workflows, storage systems, pipeline architectures, data migration techniques, and automation strategies. 

The course covers data replication and migration, extract-load (EL), extract-load-transform (ELT), extract-transform-load (ETL) pipeline patterns, streaming analytics, metadata management, and workflow automation using Google Cloud services such as BigQuery, Dataproc, Dataflow, Dataform, Cloud Composer, and Eventarc. 

Through guided labs, classroom activities, quizzes, and hands-on implementation exercises, learners will gain foundational skills for building scalable and automated data engineering solutions on Google Cloud.  

After completing Introduction to Data Engineering on Google Cloud , students will be able to:

  • Understand the role and responsibilities of a data engineer
  • Identify data engineering tasks and pipeline patterns on Google Cloud
  • Understand data sources, sinks, and storage formats
  • Load and manage datasets in BigQuery
  • Explore data migration and replication solutions on Google Cloud
  • Build EL, ELT, and ETL data pipelines
  • Understand streaming analytics and real-time processing workflows
  • Utilize Dataproc and Dataflow for batch and streaming pipelines
  • Automate workflows using Cloud Composer, Workflows, and Eventarc
  • Build scalable and automated data engineering solutions on Google Cloud

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Key features of Introduction to Data Engineering on Google Cloud:

  • Introduction to Data Engineering on Google Cloud 

     

  • Hands-On Learning Experience 

     

  • Data Replication and Migration 

     

  •  BigQuery and Data Lake Workflows 

     

  •  ETL and Streaming Pipeline Development 

     

  • Automation and Orchestration 

     

  • Google Cloud Data Engineering Ecosystem 

     

  • Beginner-Friendly Data Engineering Enablement 

     

Who Should Attend Introduction to Data Engineering on Google Cloud

  • Data Engineers
  • Database Administrators
  • System Administrators
  • Analytics Professionals
  • Cloud Engineers
  • Developers working with analytics and ETL workflows
  • Professionals beginning with cloud data engineering

Prerequisites of Introduction to Data Engineering on Google Cloud

  • Prior Google Cloud experience at the foundational level
  • Familiarity with Cloud Shell and Google Cloud Console
  • Basic proficiency with SQL
  • Experience with data modeling and ETL concepts.
  • Basic programming experience using Python or similar languages

    • Why choose CloudThat as your training partner for Introduction to Data Engineering on Google Cloud

    • Specialized GCP Data Engineering Focus  CloudThat specializes in cloud and data engineering technologies and delivers focused Google Cloud training programs with practical implementation experience and enterprise analytics use cases. 
    • Industry-Recognized Trainers  Our trainers are certified Google Cloud professionals with expertise in BigQuery, Dataflow, Dataproc, cloud analytics, streaming pipelines, and workflow automation. 
    • Hands-On Learning Approach  CloudThat emphasizes practical learning through guided labs, pipeline development exercises, streaming analytics workflows, and orchestration implementation scenarios. 
    • Customized Learning Paths  Training programs are designed for data engineers, analysts, developers, administrators, and cloud professionals with varying levels of analytics expertise. 
    • Interactive Learning Experience  Sessions include demonstrations, collaborative pipeline exercises, troubleshooting activities, quizzes, and interactive discussions. 
    • Placement Assistance and Career Support  CloudThat supports learners with cloud analytics learning paths, interview preparation, career guidance, and practical data engineering implementation strategies. 
    • Continuous Learning and Updates  Course content is continuously updated to align with the latest advancements in Google Cloud analytics, BigQuery, Dataflow, Dataproc, and orchestration technologies. 
    • Positive Reviews and Testimonials  Thousands of professionals and enterprises trust CloudThat for advanced cloud, analytics, data engineering, and Google Cloud training programs. 

    Learning Objective of Introduction to Data Engineering on Google Cloud

    • This course enables learners to understand the fundamentals of data engineering on Google Cloud and build scalable, automated, and cloud-native data pipelines using Google Cloud analytics and orchestration services. 

    Course Outline of Introduction to Data Engineering on Google Cloud Download Course Outline

    Lecture Content

    • The role of a data engineer
    • Data sources versus data sinks
    • Data formats
    • Storage solution options on Google Cloud
    • Metadata management options on Google Cloud
    • Sharing datasets using Analytics Hub

    Learning Objectives

    • Explain the role of a data engineer
    • Understand data sources and data sinks
    • Compare different data formats
    • Explore storage solution options on Google Cloud
    • Understand metadata management concepts
    • Share datasets using Analytics Hub

    Lab Content

    • Lab: Loading Data into BigQuery

    Activities

    • Quiz

    Lecture Content

    • Replication and Migration Architecture
    • The gcloud Command-Line Tool
    • Moving Datasets
    • Datastream
    • Storage Transfer Service
    • Transfer Appliance

    Learning Objectives

    • Understand Google Cloud replication and migration architectures
    • Explore Storage Transfer Service and Transfer Appliance use cases
    • Understand Datastream features and deployment workflows
    • Use gcloud for data movement operations

    Lab Content

    • Lab: Datastream – PostgreSQL Replication to BigQuery (Optional for ILT)

    Activities

    • Quiz

    Lecture Content

    • Extract and load architecture
    • The bq command-line tool
    • BigQuery Data Transfer Service
    • BigLake

    Learning Objectives

    • Understand extract-load (EL) architecture patterns
    • Use the bq command-line tool for BigQuery operations
    • Explore BigQuery Data Transfer Service workflows
    • Understand BigLake as a non-extract-load pattern

    Lab Content

    • Lab: BigLake – Qwik Start

    Activities

    • Quiz

    Lecture Content

    • Extract, load, and transform (ELT) architecture
    • SQL scripting and scheduling with BigQuery
    • Dataform

    Learning Objectives

    • Understand ELT architecture and workflows
    • Use BigQuery SQL scripting and scheduling features
    • Explore Dataform for transformation workflows
    • Build SQL-based transformation pipelines

    Lab Content

    • Lab: Create and Execute a SQL Workflow in Dataform

    Activities

    • Quiz

    Topics

    • Extract, transform, and load (ETL) architecture
    • Google Cloud GUI tools for ETL data pipelines
    • Batch data processing using Dataproc
    • Streaming data processing options
    • Bigtable and data pipelines

    Learning Objectives

    • Understand ETL architecture and workflows
    • Explore Dataproc for batch data processing
    • Use Dataproc Serverless for Spark-based ETL
    • Understand streaming pipeline options using Dataflow
    • Explore Bigtable use cases in real-time pipelines

    Lab Content

    • Lab: Use Dataproc Serverless for Spark to Load BigQuery (Optional for ILT)
    • Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

    Activities

    • Quiz

    Lecture Content

    • Automation patterns and options for pipelines
    • Cloud Scheduler and Workflows
    • Cloud Composer
    • Cloud Run Functions
    • Eventarc

    Learning Objectives

    • Understand automation patterns for data pipelines
    • Explore Cloud Scheduler and Workflows orchestration
    • Use Cloud Composer for workflow automation
    • Understand Eventarc event-driven architectures
    • Explore Cloud Run functions for automation workflows

    Lab Content

    • Lab: Use Cloud Run Functions to Load BigQuery (Optional for ILT)

    Activities

    • Quiz

    Certification details of Introduction to Data Engineering on Google Cloud

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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    FAQs of Introduction to Data Engineering on Google Cloud

    This course is designed for data engineers, administrators, developers, and professionals interested in cloud-native data engineering on Google Cloud.

    Yes. Basic Google Cloud knowledge and familiarity with Cloud Shell and the Console are recommended.

    The course covers data replication, migration, BigQuery, BigLake, Datastream, ETL/ELT pipelines, Dataflow, Dataproc, Cloud Composer, and automation workflows.

    Yes. The course includes labs involving BigQuery, BigLake, Datastream, Dataform, Dataproc, Dataflow, and Cloud Run Functions.

    BigQuery, BigLake, Datastream, Dataproc, Dataflow, Cloud Composer, Eventarc, Dataform, Analytics Hub, and Cloud Run Functions.

    Yes. Learners will build streaming data pipelines using Dataflow.

    Yes. The course covers Cloud Composer, Workflows, Cloud Scheduler, Eventarc, and Cloud Run Functions.

    Instructor-led training (1 day) or on-demand learning (8 hours) with lectures, labs, quizzes, and classroom activities.

    Yes. A CloudThat Course Completion Certificate will be awarded after successful completion of the training.

    Yes. This is an introductory-level course designed for professionals beginning their journey into cloud data engineering on Google Cloud.

    Enquire Now