Google Cloud Data Pipelines and Integration Training Course

 Google Cloud Data Pipelines and Integration Training Course 

Introduction 

Google Cloud Data Pipelines and Integration Training Course provides comprehensive practical knowledge and technical skills in designing, developing, deploying, and managing scalable cloud-based data pipelines. The course explores Google Cloud data engineering technologies, including BigQuery, Cloud Dataflow, Pub/Sub, Cloud Storage, Dataproc, Cloud Composer, and Data Fusion. Participants gain expertise in data ingestion, ETL and ELT workflows, real-time data streaming, batch processing, data transformation, workflow orchestration, and enterprise data integration. Emphasis is placed on cloud-native architecture, automated data processing, serverless computing, data quality management, and secure data movement across distributed environments. 

The training integrates practical exercises and global industry case studies to demonstrate how organizations leverage Google Cloud Platform to modernize data infrastructure, optimize analytics, and support data-driven decision-making. Participants explore modern data architecture, scalable pipeline development, API integration, event-driven processing, monitoring, and performance optimization. The course also addresses cloud security, governance, cost management, and integration with enterprise applications. By the end of the programme, participants will be equipped to implement reliable, scalable, and cost-effective Google Cloud data pipelines that support business intelligence, artificial intelligence, machine learning, and enterprise digital transformation initiatives. 

Course Objectives 

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

1. Understand Google Cloud data engineering architecture and services.
2. Design scalable cloud-native data pipelines.
3. Implement automated ETL and ELT workflows.
4. Develop real-time data streaming solutions.
5. Apply BigQuery for advanced data processing and analytics.
6. Configure Cloud Dataflow for batch and streaming integration.
7. Implement event-driven architectures using Pub/Sub.
8. Integrate enterprise applications through APIs.
9. Apply data orchestration using Cloud Composer.
10. Implement data quality, validation, and transformation.
11. Strengthen cloud data security and governance.
12. Optimize pipeline performance and cloud infrastructure costs.
13. Deploy automated monitoring and troubleshooting solutions.

Organizational Benefits
 

1. Reduced data integration and infrastructure operational costs.
2. Improved enterprise data accessibility and interoperability.
3. Faster business intelligence and reporting processes.
4. Enhanced real-time analytics and decision-making.
5. Improved data quality and operational reliability.
6. Scalable infrastructure for growing business workloads.
7. Strengthened cloud security and regulatory compliance.
8. Increased automation of enterprise data operations.
9. Improved collaboration between IT and analytics teams.
10. Accelerated digital transformation and innovation.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Big data developers and database administrators.
3. Business intelligence and analytics professionals.
4. Cloud computing and DevOps engineers.
5. Software developers and integration specialists.
6. IT managers and enterprise technology consultants.
7. Data scientists and machine learning engineers.
8. Digital transformation and technology project managers.

Course Duration: 5 days

Course Modules

Module 1: Google Cloud Data Engineering Fundamentals
 

• Introduction to Google Cloud Platform and data engineering.
• Cloud-native data architecture and service ecosystems.
• Google Cloud Storage and data lake fundamentals.
• Data pipeline lifecycle and architectural design.
• Enterprise cloud adoption and integration strategies.
• Case study: Spotify's cloud-based data analytics and processing ecosystem.

Module 2: Data Ingestion and Integration Technologies
 

• Batch and real-time data ingestion techniques.
• Integrating structured and unstructured data sources.
• Google Cloud Data Fusion and connector configuration.
• API integration and automated data extraction.
• Data transfer using Storage Transfer Service.
• Case study: HSBC's enterprise data integration and modernization initiatives.

Module 3: Building Data Pipelines with Cloud Dataflow
 

• Introduction to Apache Beam and Cloud Dataflow.
• Developing scalable batch processing pipelines.
• Implementing real-time streaming transformations.
• Windowing, triggers, and data aggregation.
• Pipeline deployment and execution optimization.
• Case study: PayPal-style real-time transaction processing and fraud analytics.

Module 4: Real-Time Data Streaming with Pub/Sub
 

• Google Cloud Pub/Sub architecture and messaging.
• Event-driven data processing and asynchronous communication.
• Topic, subscription, and message management.
• Streaming integration with BigQuery and Dataflow.
• Error handling and message delivery reliability.
• Case study: Uber's real-time event processing and transportation analytics.

Module 5: BigQuery and Advanced Data Processing
 

• BigQuery architecture and data warehouse fundamentals.
• SQL-based data transformation and analytics.
• External tables, partitioning, and clustering.
• Data integration using scheduled queries and pipelines.
• Query performance and cost optimization.
• Case study: Spotify's large-scale analytics and data warehousing applications.

Module 6: Workflow Orchestration and Automation
 

• Introduction to Cloud Composer and Apache Airflow.
• Designing automated data workflows and dependencies.
• Scheduling and monitoring pipeline execution.
• Managing workflow failures and task retries.
• Integrating orchestration with Google Cloud services.
• Case study: Airbnb's automated data workflow and analytics operations.

Module 7: Data Governance, Security and Quality Management
 

• Google Cloud Identity and Access Management.
• Data encryption and secure pipeline configurations.
• Data validation, profiling, and quality assurance.
• Governance using Dataplex and Data Catalog capabilities.
• Compliance, auditing, and access management.
• Case study: Financial institutions implementing secure cloud data governance.

Module 8: Pipeline Deployment, Monitoring and Optimization
 

• Deploying production-ready data pipelines.
• Monitoring with Google Cloud Operations.
• Logging, troubleshooting, and error management.
• CI/CD automation and infrastructure management.
• Cost optimization, scalability, and performance testing.
• Case study: Retail enterprises implementing scalable cloud analytics pipelines.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical Google Cloud Platform laboratory exercises.
• Guided data pipeline development and integration projects.
• Group discussions and international industry case studies.
• Hands-on troubleshooting and performance optimization.
• Practical assessments and participant project presentations.

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.