Master the Google Cloud Professional Data Engineer certification with this comprehensive course, covering essential topics like data processing design, pipeline development, machine learning operationalization, and system scalability.
Key Skills You’ll Gain:
- Designing data processing systems from scratch
- Operationalizing machine learning models
- Ensuring quality and reliability in data solutions
- Building and scaling storage systems and processing infrastructure
- Migrating and optimizing data warehousing solutions
- Deploying and monitoring machine learning pipelines
- Implementing security, compliance, and scalability in data projects
Course Requirements:
All the essential knowledge and skills needed to pass the Google Cloud Professional Data Engineer certification exam will be provided.
What’s Included:
- 25.5 hours of on-demand video content
- 51 downloadable resources
- Access on mobile and TV
- Lifetime access
- Certificate of completion
Course Breakdown:
Data Processing System Design
- Storage Technologies: Selecting storage options based on business needs, data modeling, latency, and transaction requirements.
- Schema Design: Understanding schema and distributed system considerations.
Data Pipeline Development
- Data Flow Management: Covering data publishing, batch, and streaming workflows (e.g., BigQuery, Dataflow, Dataproc).
- Automation and Orchestration: Leveraging tools like Cloud Composer to streamline jobs.
Data Processing Solution Design
- Infrastructure Selection: Deciding on architecture, system availability, capacity planning, and hybrid cloud solutions.
- Event Processing: Implementing event handling strategies for varied processing needs.
Data Warehousing Migration
- Cloud Migration: Migrating from on-premises setups to cloud infrastructure with Google services.
- Validation: Ensuring accuracy and reliability during migration.
Building Data Processing Systems
- Storage Systems: Utilizing managed services (e.g., Cloud Bigtable, BigQuery) for effective data management.
- Pipelines and Infrastructure: Integrating data cleansing, transformation, and monitoring techniques.
Machine Learning Operationalization
- Pre-built ML Models: Working with ML APIs (Vision, Speech, AutoML).
- ML Pipeline Deployment: Ingesting data, retraining models, and utilizing tools like BigQuery ML.
Solution Quality Assurance
- Security and Compliance: Ensuring IAM, data security, and compliance (GDPR, HIPAA).
- Scalability: Testing, monitoring, and scaling resources for efficient processing.
Reliability and Flexibility
- Data Preparation and Quality Control: Utilizing tools like Dataprep.
- Flexibility: Designing for data portability and multicloud compatibility.
Who Should Take This Course:
This course is suitable for beginners, intermediates, and advanced users looking to master data engineering on Google Cloud.
