Google Cloud Data Lake and Lakehouse Architecture Training Course

 Google Cloud Data Lake and Lakehouse Architecture Training Course 

Introduction 

Google Cloud Data Lake and Lakehouse Architecture Training Course provides comprehensive practical knowledge and technical expertise in designing, developing, implementing, and managing modern cloud-based data lake and lakehouse environments using Google Cloud Platform. The course explores cloud data engineering, enterprise data architecture, data lake storage, lakehouse design principles, scalable data pipelines, data ingestion, data transformation, metadata management, and data governance. Participants gain practical experience with Google Cloud Storage, BigQuery, Dataproc, Dataflow, Pub/Sub, Dataplex, and related technologies to build secure, scalable, cost-effective, and high-performance enterprise data platforms. 

The training emphasizes modern data management strategies, data lakehouse architecture, real-time analytics, serverless data processing, machine learning integration, and enterprise business intelligence. Through practical exercises and international case studies, participants learn to integrate structured, semi-structured, and unstructured datasets, optimize analytical workloads, implement data quality frameworks, and establish effective cloud data governance. The course prepares professionals to support digital transformation, advanced analytics, artificial intelligence, business intelligence, and data-driven decision-making across global organizations. 

Course Objectives 

• Understand Google Cloud data lake and lakehouse architecture.
• Design scalable enterprise cloud data platforms.
• Implement cloud-native data ingestion pipelines.
• Apply BigQuery for advanced analytics.
• Manage structured and unstructured datasets.
• Develop real-time data processing solutions.
• Implement metadata management and data governance.
• Optimize cloud storage and processing costs.
• Integrate machine learning with lakehouse environments.
• Apply data security and access management.
• Implement data quality and lifecycle management.
• Develop automated data engineering workflows.
• Evaluate enterprise data architecture strategies.

Organizational Benefits
 

• Improved enterprise data management and accessibility.
• Reduced infrastructure and data processing costs.
• Enhanced business intelligence and reporting.
• Faster data-driven decision-making.
• Improved data security and regulatory compliance.
• Scalable cloud-based analytics infrastructure.
• Better integration of organizational data sources.
• Increased operational efficiency and productivity.
• Enhanced artificial intelligence readiness.
• Improved enterprise digital transformation capabilities.

Target Audiences
 

• Cloud data engineers and architects.
• Big data engineers and developers.
• Database administrators and managers.
• Business intelligence professionals.
• Data scientists and machine learning engineers.
• IT infrastructure and cloud administrators.
• Enterprise technology consultants.
• Data governance and analytics managers.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Google Cloud Data Lake and Lakehouse Architecture
 

• Data lake, data warehouse, and lakehouse concepts.
• Google Cloud data architecture components.
• Enterprise cloud data management principles.
• Lakehouse architecture and implementation strategies.
• Storage and analytical technology integration.
• Case Study: Google's cloud-based analytics ecosystem.

Module 2: Google Cloud Storage and Data Lake Design
 

• Google Cloud Storage architecture and capabilities.
• Data lake storage classes and lifecycle policies.
• Bucket configuration and access management.
• Structured, semi-structured, and unstructured storage.
• Data partitioning and storage optimization.
• Case Study: Spotify's large-scale data storage and analytics approach.

Module 3: BigQuery and Lakehouse Analytics
 

• BigQuery architecture and data processing.
• Data warehousing and lakehouse integration.
• SQL analytics and query optimization.
• External tables and BigLake integration.
• Partitioning, clustering, and cost management.
• Case Study: Retail analytics using Google BigQuery.

Module 4: Data Ingestion and Pipeline Engineering
 

• Batch and real-time data ingestion.
• Cloud Dataflow and Apache Beam integration.
• Pub/Sub event-driven data pipelines.
• Data extraction, transformation, and loading.
• Pipeline monitoring and automation.
• Case Study: Global financial transaction data integration.

Module 5: Data Processing and Transformation
 

• Dataproc and Apache Spark processing.
• Data cleaning and transformation techniques.
• Distributed data processing frameworks.
• Data orchestration and workflow automation.
• Data quality validation and monitoring.
• Case Study: Uber's large-scale distributed data processing approach.

Module 6: Lakehouse Governance, Security and Metadata Management
 

• Dataplex and enterprise data governance.
• Metadata cataloguing and data discovery.
• Identity and Access Management.
• Data classification and protection.
• Compliance, auditing, and access policies.
• Case Study: Enterprise financial data governance implementation.

Module 7: Advanced Analytics and Machine Learning Integration
 

• BigQuery machine learning capabilities.
• Vertex AI integration with data lakes.
• Predictive analytics and model development.
• Feature engineering and data preparation.
• AI-driven enterprise analytics.
• Case Study: Retail demand forecasting using cloud analytics and machine learning.

Module 8: Enterprise Lakehouse Optimization and Implementation
 

• Lakehouse performance optimization.
• Data lifecycle and storage management.
• Cloud cost monitoring and optimization.
• Disaster recovery and business continuity.
• Enterprise architecture implementation strategies.
• Case Study: Global enterprise cloud modernization using Google Cloud.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical Google Cloud laboratory exercises.
• Guided data lake and lakehouse architecture projects.
• Group discussions and international case study analysis.
• Hands-on data engineering and analytics assignments.
• Interactive assessments and knowledge evaluation.

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.