Google Cloud DataOps Training Course

 Google Cloud DataOps Training Course 

Introduction 

Google Cloud DataOps Training Course provides comprehensive practical knowledge and technical skills for implementing modern data operations using Google Cloud Platform. The course focuses on cloud data engineering, DataOps automation, data pipeline orchestration, continuous integration and continuous delivery (CI/CD), data quality management, data governance, and real-time data processing. Participants will explore Google Cloud technologies, including BigQuery, Cloud Composer, Dataflow, Pub/Sub, Cloud Storage, and Dataplex, to develop scalable, secure, reliable, and cost-effective data ecosystems. The training integrates modern data engineering practices, DevOps principles, automation frameworks, and cloud-native architectures to improve enterprise data management and analytics capabilities. 

The course also examines how organizations can establish automated data workflows, implement infrastructure as code, monitor data pipelines, and maintain consistent data quality across complex cloud environments. Through practical exercises and global industry case studies, participants will learn to streamline data integration, accelerate analytics delivery, optimize cloud resources, and establish collaborative data operations. Emphasis is placed on Google Cloud DataOps best practices, machine learning integration, metadata management, observability, and enterprise data lifecycle management to support digital transformation and data-driven decision-making. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand Google Cloud DataOps architecture and principles.
2. Develop automated cloud data pipelines and workflows.
3. Implement CI/CD practices for data engineering.
4. Apply infrastructure as code for cloud deployments.
5. Manage data quality, validation, and observability.
6. Configure BigQuery for scalable enterprise analytics.
7. Implement real-time data ingestion and processing.
8. Automate data orchestration using Cloud Composer.
9. Apply cloud data governance and security frameworks.
10. Integrate DevOps automation into data engineering.
11. Monitor pipeline performance and operational reliability.
12. Optimize cloud infrastructure and data processing costs.
13. Implement scalable DataOps strategies for digital transformation.

Organizational Benefits
 

1. Improved enterprise data pipeline automation and efficiency.
2. Reduced operational costs through cloud resource optimization.
3. Enhanced data quality, consistency, and reliability.
4. Faster analytics delivery and business intelligence.
5. Improved collaboration between data engineering and IT teams.
6. Strengthened cloud security and regulatory compliance.
7. Reduced deployment errors through CI/CD automation.
8. Increased scalability of enterprise data infrastructure.
9. Better visibility through centralized monitoring and observability.
10. Accelerated data-driven innovation and digital transformation.

Target Audiences
 

1. Data engineers and cloud data architects.
2. DevOps and DataOps engineers.
3. Cloud infrastructure administrators.
4. Business intelligence and analytics professionals.
5. Database administrators and data managers.
6. Software developers and systems engineers.
7. IT project managers and technical consultants.
8. Enterprise data governance and quality specialists.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Google Cloud DataOps
 

• DataOps principles, architecture, and operating models.
• Google Cloud data ecosystem and core services.
• Data lifecycle management and automation.
• Collaboration between development and operations teams.
• DataOps maturity models and implementation strategies.
• Case study: Google's cloud-based data operations and analytics ecosystem.

Module 2: Google Cloud Data Engineering Infrastructure
 

• Google Cloud Storage and data lake architecture.
• BigQuery data warehouse configuration.
• Cloud networking, IAM, and resource management.
• Data integration and cloud storage strategies.
• Infrastructure scalability and resource provisioning.
• Case study: Spotify's cloud data infrastructure and large-scale analytics practices.

Module 3: Data Pipeline Development and Automation
 

• Designing batch and streaming data pipelines.
• Data ingestion using Pub/Sub.
• Data transformation using Dataflow.
• Pipeline automation and dependency management.
• Data integration and workflow optimization.
• Case study: Real-time data processing for financial services using Google Cloud technologies.

Module 4: Data Orchestration with Cloud Composer
 

• Introduction to Apache Airflow and Cloud Composer.
• Workflow scheduling and task dependencies.
• DAG development and execution.
• Automated pipeline monitoring and error handling.
• Workflow optimization and recovery strategies.
• Case study: Enterprise workflow orchestration for large-scale retail analytics.

Module 5: CI/CD and Infrastructure as Code
 

• Continuous integration and continuous delivery principles.
• Automated testing and deployment workflows.
• Infrastructure provisioning using Terraform.
• Git-based version control and collaboration.
• Deployment automation and environment management.
• Case study: Netflix-inspired continuous delivery practices for reliable cloud deployments.

Module 6: Data Quality, Governance, and Security
 

• Data validation and automated quality testing.
• Metadata management using Dataplex.
• Data lineage and catalog management.
• IAM, encryption, and access control.
• Regulatory compliance and governance frameworks.
• Case study: Financial institution data governance and quality management using cloud platforms.

Module 7: Data Observability and Performance Optimization
 

• Pipeline monitoring and operational observability.
• Cloud Logging and Cloud Monitoring.
• Data freshness, reliability, and anomaly detection.
• BigQuery performance and query optimization.
• Cloud cost management and resource efficiency.
• Case study: Global e-commerce data platform optimization and operational monitoring.

Module 8: Advanced DataOps Implementation and Capstone
 

• Designing enterprise Google Cloud DataOps architectures.
• Integrating machine learning into data workflows.
• Automated incident management and recovery.
• DataOps performance assessment and continuous improvement.
• Developing enterprise implementation roadmaps.
• Case study: Building an automated cloud analytics platform for a multinational organization.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical Google Cloud laboratory exercises.
• Hands-on data pipeline development and automation.
• Group discussions and collaborative technical workshops.
• Global industry case studies and scenario-based learning.
• Capstone projects, assessments, and knowledge-sharing sessions.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.