Course Overview of Data Engineering on Google Cloud

This course provides learners with hands-on experience in designing and building modern data engineering systems on Google Cloud. Through lectures, demos, labs, and classroom activities, participants will learn how to design scalable data architectures, build batch and streaming pipelines, implement lakehouse solutions, automate workflows, and integrate AI/ML capabilities into data platforms.

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

  • Design scalable and secure data processing systems on Google Cloud.
  • Build and manage batch and streaming data pipelines.
  • Implement modern data lakehouse architectures.
  • Automate orchestration and workflow management.
  • Use BigQuery, Dataflow, Dataproc, Pub/Sub, and Bigtable effectively.
  • Apply AI/ML and analytics capabilities to data engineering workflows.
  • Implement monitoring, governance, and observability practices.
  • Build operational and analytical data platforms using Google Cloud services.

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Key Features: Data Engineering on Google Cloud Platform

  •  Modern Data Engineering Foundations

  • Lakehouse and Data Warehouse Architectures

  • Batch and Streaming Data Pipelines

  • Automation and Orchestration

  • Data Quality, Governance, and Security

  • AI/ML and Advanced Analytics Integration

  • Observability and Monitoring.

  • Real-World Hands-On Labs and Use Cases

Who Should Attend Data Engineering on Google Cloud :

  • Data Engineers
  • Data Analysts
  • Data Architects

Prerequisites of Data Engineering on Google Cloud

  • Understanding of ETL/ELT and data engineering principles
  • Familiarity with data warehouses and data lakes
  • SQL proficiency
  • Python programming recommended
  • Familiarity with CLI tools
  • Understanding of core Google Cloud services

    • Why choose CloudThat as your Training Partner?

    • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, Case-Studies, best practices, and hands-on experience tailored specifically for GCP.
    • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios.
    • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set.
    • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills.
    • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course.
    • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals.
    • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge.
    • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training.

    Learning objective of the course :

    • Design and build robust data pipelines on GCP, handling both batch and streaming data with confidence.
    • Extract valuable insights from massive datasets using BigQuery and advanced analytics tools.
    • Leverage unstructured data for valuable insights through Spark and ML integration.
    • Generate real-time insights from streaming data, fueling agile decision-making.
    • Build powerful machine learning models using Cloud AutoML and BigQuery ML, even without extensive coding experience.

    Course Outline: Download Course Outline

    Topics:

    • Role of a Data Engineer
    • Data Sources and Data Sinks
    • Data Formats
    • Storage Solutions
    • Metadata Management
    • Analytics Hub

    Learning Outcomes

    • Understand core data engineering responsibilities.
    • Compare storage and metadata management solutions.
    • Share datasets using Analytics Hub.

    Activities

    • Lab: Loading Data into BigQuery
    • Quiz

    Topics:

    • Replication and Migration Architecture
    • gcloud CLI
    • Storage Transfer Service
    • Transfer Appliance
    • Datastream

    Learning Outcomes

    • Design data replication and migration strategies.
    • Use Datastream and transfer services effectively.

    Activities

    • Migration Architecture Discussion
    • Quiz

    Topics:

    • Extract and Load Architecture
    • bq Command-Line Tool
    • BigQuery Data Transfer Service
    • BigLake

    Learning Outcomes

    • Build extract-and-load pipelines.
    • Use BigLake for non-extract-load patterns.

    Activities

    • Lab: BigLake Qwik Start
    • Quiz

    Topics

    • ELT Architecture
    • BigQuery SQL Scripting
    • Scheduling
    • Dataform

    Learning Outcomes

    • Build ELT workflows using BigQuery and Dataform.
    • Automate SQL transformations and scheduling.

    Activities

    • Lab: Create and Execute a SQL Workflow in Dataform
    • Quiz

    Topics:

    • ETL Architecture
    • GUI Tools for ETL
    • Dataproc
    • Dataproc Serverless for Spark
    • Streaming Processing
    • Bigtable Pipelines

    Learning Outcomes

    • Design ETL pipelines using Dataproc and Dataflow.
    • Implement batch and streaming processing architectures.

    Activities

    • Lab: Dataproc Serverless for Spark
    • Lab: Streaming Dashboard with Dataflow
    • Quiz

    Topics:

    • Automation Patterns
    • Cloud Scheduler
    • Workflows
    • Cloud Composer
    • Cloud Run Functions
    • Eventarc

    Learning Outcomes

    • Automate and orchestrate data workflows.
    • Implement event-driven automation solutions.

    Activities

    • Lab: Use Cloud Run Functions to Load BigQuery
    • Quiz

    Topics:

    • Data Lakes
    • Data Warehouses
    • Data Lakehouse
    • Architecture Selection

    Learning Outcomes

    • Compare modern data architecture patterns.
    • Evaluate lakehouse benefits and trade-offs.

    Activities

    • Architecture Comparison Workshop
    • Quiz

    Topics:

    • Cloud Storage Foundation
    • Apache Iceberg
    • BigQuery
    • AlloyDB
    • Federated Queries

    Learning Outcomes

    • Build unified lakehouse architectures.
    • Combine operational and analytical data.

    Activities

    • Lab: Federated Query with BigQuery
    • Quiz

    Topics:

    • BigQuery Fundamentals
    • Partitioning and Clustering
    • BigLake
    • External Tables

    Learning Outcomes

    • Build scalable cloud data warehouses.
    • Use BigLake and external tables effectively.

    Activities

    • Lab: Querying External Data and Iceberg Tables
    • Quiz

    Topics:

    • Governance and Security
    • Data Loss Prevention
    • Analytics and Machine Learning
    • Migration Strategies

    Learning Outcomes

    • Implement governance and metadata management.
    • Enable advanced analytics and AI workloads.

    Activities

    • Lab: BigQuery ML
    • Lab: Vector Search with BigQuery

    Topics:

    • Batch Pipeline Use Cases
    • Processing Challenges

    Learning Outcomes

    • Identify when to use batch processing.
    • Analyze common pipeline challenges.

    Activities

    • Batch Processing Discussion
    • Quiz

    Topics:

    • Batch Pipeline Design
    • Large-Scale Transformations
    • Dataflow
    • Serverless Spark
    • Orchestration

    Learning Outcomes

    • Build scalable batch pipelines.
    • Optimize throughput and cost efficiency.

    Activities

    • Lab: Batch Pipeline with Spark
    • Lab: Dataflow Job Builder UI
    • Quiz

    Topics:

    • Validation and Cleansing
    • Error Analysis
    • Schema Evolution
    • Deduplication

    Learning Outcomes

    • Ensure pipeline data quality and consistency.
    • Handle schema changes and duplicate data.

    Activities

    • Lab: Validate Data Quality in Batch Pipelines
    • Quiz

    Topics:

    • Cloud Composer
    • Observability
    • Alerts and Troubleshooting
    • Visual Pipeline Management

    Learning Outcomes

    • Orchestrate complex workflows.
    • Implement monitoring and troubleshooting practices.

    Activities

    • Lab: Batch Pipelines in Cloud Data Fusion
    • Quiz

    Topics:

    • Streaming Pipeline Concepts
    • Business Use Case
    • Challenges and Mission

    Learning Outcomes

    • Understand streaming pipeline requirements.
    • Identify streaming architecture challenges.

    Activities

    • Use Case Discussion
    • Architecture Overview

    Topics:

    • Streaming ETL
    • Streaming AI/ML
    • Streaming Applications
    • Reverse ETL

    Learning Outcomes

    • Design streaming architectures for different use cases.
    • Compare streaming pipeline patterns.

    Activities

    • Streaming Architecture Workshop
    • Quiz

    Topics:

    • Pub/Sub
    • Managed Service for Apache Kafka
    • Dataflow
    • Streaming Processing

    Learning Outcomes

    • Select appropriate streaming technologies.
    • Build real-time streaming pipelines.

    Activities

    • Lab: Stream Data Pipelines – Esports Use Case
    • Quiz

    Topics:

    • BigQuery Streaming
    • Continuous Queries
    • Reverse ETL
    • Bigtable Integration

    Learning Outcomes

    • Build streaming analytics solutions.
    • Integrate operational and analytical streaming systems.

    Activities

    • Lab: Apache Beam and Bigtable Integration
    • Lab: Pub/Sub and BigQuery Streaming
    • Quiz

    Topics

    • Course Summary
    • Next Steps

    Learning Outcomes

    • Consolidate modern data engineering concepts.
    • Review best practices and architecture patterns.

    Activities

    • Final Review Session
    • Q&A Discussion

    Certification Details of Data Engineering on Google Cloud

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

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    Course ID: 19458

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    FAQs for Data Engineering on Google Cloud

    This course is designed for data engineers, analysts, and architects building modern data platforms on Google Cloud.

    Yes. The course includes multiple labs, architecture workshops, pipeline exercises, and real-world streaming scenarios.

    The course covers BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, BigLake, Vertex AI, and several Google Cloud data services.

    Yes. Streaming ETL, streaming analytics, reverse ETL, Pub/Sub, Kafka, and Dataflow are major focus areas.

    Yes. Learners will explore modern lakehouse architectures using BigLake, BigQuery, and Apache Iceberg.

    The course is available in Instructor-Led Training (ILT) and On-Demand formats.

    Yes. SQL proficiency and familiarity with Python are recommended.

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

    Enquire Now