Introduction

The Google Cloud Certified Professional Data Engineer certification is designed for professionals who design, build, and maintain data processing systems, analyze data, and make data-driven decisions in a cloud environment. This 5-day intensive training course prepares participants for the certification exam by covering Google Cloud’s data engineering tools and services, including BigQuery, Dataflow, Pub/Sub, Dataproc, and more. Participants will learn how to manage and optimize data workflows, build scalable data solutions, and implement machine learning models, enabling them to become proficient data engineers in Google Cloud environments.

Course Objectives

By the end of this training, participants will:

1. Understand how to design, build, and manage scalable data processing systems on Google Cloud.
2. Gain expertise in using Google Cloud tools and services for data storage, transformation, analysis, and visualization.
3. Develop skills in data engineering workflows, including data pipelines, real-time data processing, and data modeling.
4. Learn to optimize and maintain cloud data infrastructure and ensure security and compliance.
5. Be fully prepared to take the Professional Data Engineer certification exam and advance their career in cloud data engineering.

Who Should Attend?

This course is ideal for:

• Data engineers, data architects, and IT professionals who are responsible for building and managing data systems on Google Cloud.
• Professionals with experience in data engineering, data analysis, or machine learning who want to formalize their expertise in Google Cloud.
• Individuals preparing for the Google Cloud Professional Data Engineer certification exam.
• Those looking to learn about best practices for designing and implementing data solutions in a cloud-based environment.

Day 1: Introduction to Google Cloud and Data Engineering Fundamentals

• Session 1: Overview of Google Cloud and Cloud Data Engineering

  • Introduction to Google Cloud Platform (GCP): Core services and infrastructure
  • The role of a data engineer on GCP: Responsibilities and skills required
  • Google Cloud services for data engineering: BigQuery, Dataflow, Pub/Sub, Dataproc, etc.
  • Overview of data engineering principles: Data pipelines, transformation, and ETL (Extract, Transform, Load)

• Session 2: Data Storage on Google Cloud

  • Understanding Google Cloud Storage: Object storage, versioning, and access control
  • BigQuery: Managed data warehouse for scalable and fast querying of large datasets
  • Google Cloud SQL and NoSQL databases: Firebase, Cloud Spanner, and Cloud Datastore
  • Choosing the right storage solution for your data: Structured vs. unstructured data

• Session 3: Data Processing with Google Cloud

  • Introduction to Dataflow: Managed stream and batch data processing
  • Using Dataproc for big data processing: Apache Spark and Hadoop on GCP
  • Real-time data processing with Pub/Sub
  • Data pipeline orchestration with Cloud Composer

Day 2: Building Data Pipelines and Dataflow Optimization

• Session 4: Building Data Pipelines on Google Cloud

  • Understanding the data pipeline architecture: Data ingestion, transformation, storage, and analysis
  • Creating and managing data pipelines with Google Cloud Dataflow
  • Data transformation using Apache Beam on Dataflow
  • Monitoring, troubleshooting, and logging pipelines in Cloud Dataflow

• Session 5: Batch and Stream Processing

  • Overview of batch vs. real-time (stream) data processing
  • Dataflow for batch processing and parallelism
  • Using Pub/Sub for stream processing and integration with Dataflow
  • Best practices for scaling data pipelines with cloud resources

• Session 6: Dataflow Performance Optimization

  • Optimizing Dataflow pipelines: Autoscaling, windowing, and triggering
  • Reducing pipeline costs: Managing resources and optimizing data throughput
  • Managing and handling failures in streaming and batch processes
  • Cost optimization strategies: Data transfer, storage, and compute costs

Day 3: Data Analysis and Machine Learning

• Session 7: Analyzing Data with BigQuery

  • Introduction to BigQuery: Architecture, storage, and querying
  • Data analysis with BigQuery SQL: Advanced queries, joins, window functions, and more
  • Best practices for querying and optimizing BigQuery performance
  • Integrating BigQuery with other services: Dataflow, Cloud Storage, and Cloud Machine Learning Engine

• Session 8: Machine Learning on Google Cloud

  • Overview of Google Cloud AI and ML tools: Cloud AI Platform, AutoML, and TensorFlow
  • Building machine learning models with Cloud AI Platform
  • Using BigQuery ML for machine learning on structured data
  • Deploying and managing ML models on Google Cloud

• Session 9: Data Visualization and Reporting

  • Visualizing data with Google Data Studio
  • Creating interactive dashboards with Google Data Studio and BigQuery
  • Integrating Google Cloud with third-party visualization tools (e.g., Tableau, Power BI)
  • Best practices for data storytelling and insights

Day 4: Security, Compliance, and Data Governance

• Session 10: Data Security and Access Control

  • Understanding Google Cloud security: IAM (Identity and Access Management), encryption, and secure access
  • Best practices for securing data in transit and at rest
  • Data governance in Google Cloud: Auditing, logging, and access control policies
  • Managing sensitive data and compliance requirements on GCP

• Session 11: Data Governance on Google Cloud

  • Implementing data quality management: Monitoring data quality and integrity
  • Data cataloging and metadata management with Data Catalog
  • Managing privacy and regulatory compliance: GDPR, CCPA, HIPAA, and more
  • Role of the data engineer in implementing governance practices

• Session 12: Data Architecture Design

  • Designing data architectures for scalability, reliability, and cost-efficiency
  • Building cost-effective data pipelines and storage solutions
  • Ensuring data resilience: Disaster recovery, fault tolerance, and backups
  • Managing lifecycle of data: Retention policies, data archiving, and deletion

Day 5: Exam Review, Case Studies, and Final Preparation

• Session 13: Case Studies and Real-World Applications

  • Real-world case studies: Building data engineering solutions on Google Cloud
  • Architecting end-to-end data pipelines: Ingestion, processing, and visualization
  • Designing for high availability and disaster recovery in cloud environments

• Session 14: Professional Data Engineer Exam Review

  • Comprehensive review of all key exam topics: Data engineering, machine learning, security, analytics, and cloud architectures
  • Practice exam questions and detailed discussion of answers
  • Addressing the most common areas of difficulty for the exam

• Session 15: Final Exam Preparation

  • Exam-taking strategies: Time management, question analysis, and focus areas
  • Final Q&A session to address any remaining questions
  • Personalized exam preparation tips and advice