Modern ELT Architecture and Implementation Training Course

 Modern ELT Architecture and Implementation Training Course 

Introduction 

Modern Extract, Load, and Transform (ELT) architecture is transforming how organizations collect, integrate, process, and manage data in modern cloud-based environments. Modern ELT Architecture and Implementation Training Course provides practical knowledge and technical expertise in designing scalable data pipelines, implementing cloud-native data integration frameworks, and building high-performance analytical data platforms. Participants will explore modern data engineering technologies, cloud data warehouses, data orchestration, transformation frameworks, metadata management, data quality automation, and enterprise data governance. The course emphasizes industry-leading technologies such as dbt, Snowflake, Google BigQuery, Amazon Redshift, Apache Airflow, Fivetran, and Databricks to support modern data architecture and business intelligence requirements. 

The training also focuses on implementing end-to-end ELT workflows, optimizing data transformation processes, automating pipeline execution, and establishing reliable data operations for enterprise environments. Participants will develop practical competencies in incremental data loading, change data capture, dimensional modeling, data observability, performance optimization, and scalable cloud analytics. Through international industry case studies and hands-on exercises, learners will understand how global organizations modernize legacy data warehouses, implement cloud-first data strategies, and establish trusted data ecosystems that support artificial intelligence, machine learning, real-time analytics, and data-driven decision-making. 

Course Objectives 

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

1. Understand modern ELT architecture, cloud-native data engineering, and enterprise data integration.
2. Design scalable ELT pipelines using modern data engineering frameworks.
3. Implement automated data ingestion and transformation workflows.
4. Configure cloud data warehouses for enterprise analytics.
5. Apply dbt for modular SQL transformations and analytics engineering.
6. Implement incremental loading and change data capture strategies.
7. Integrate heterogeneous data sources using automated ingestion tools.
8. Develop efficient data orchestration and workflow automation.
9. Apply data quality testing, validation, and observability frameworks.
10. Optimize query execution, warehouse performance, and cloud computing costs.
11. Implement metadata management, lineage tracking, and data governance.
12. Integrate ELT platforms with artificial intelligence and machine learning workflows.
13. Develop production-ready ELT solutions using DevOps and continuous integration practices.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced operational costs through automated data processing.
3. Faster business intelligence and analytics delivery.
4. Enhanced data quality, accuracy, and reliability.
5. Scalable cloud data infrastructure.
6. Improved regulatory compliance and data governance.
7. Better decision-making through trusted analytical datasets.
8. Increased productivity through automated workflows.
9. Improved collaboration between data engineers and analytics teams.
10. Stronger organizational readiness for artificial intelligence and advanced analytics.

Target Audiences
 

1. Data engineers and analytics engineers.
2. Cloud architects and data architects.
3. Database administrators and data warehouse specialists.
4. Business intelligence developers and analysts.
5. Data scientists and machine learning engineers.
6. Information technology managers and technical leads.
7. Enterprise data governance and integration specialists.
8. Software developers and cloud computing professionals.

Course Duration: 5 days
 
Course Modules

Module 1: Foundations of Modern ELT Architecture
 

• Introduction to Extract, Load, and Transform principles.
• Differences between traditional ETL and modern ELT architectures.
• Cloud-native data platforms and modern data stack components.
• ELT architecture patterns, scalability, and data processing layers.
• Data lake, data warehouse, and lakehouse integration.
• Case Study: Netflix's cloud-based data infrastructure and large-scale analytical data processing.

Module 2: Data Ingestion and Integration Technologies
 

• Batch ingestion and automated data extraction techniques.
• API integration and database connectivity.
• Implementing Fivetran and Airbyte for automated ingestion.
• Change data capture and incremental data synchronization.
• Managing structured, semi-structured, and unstructured data.
• Case Study: Uber's large-scale data ingestion and integration ecosystem.

Module 3: Cloud Data Warehousing and Storage
 

• Snowflake, Google BigQuery, and Amazon Redshift architecture.
• Cloud storage optimization and data partitioning.
• Data warehouse schemas and storage management.
• Implementing scalable cloud data processing environments.
• Warehouse security, access controls, and resource management.
• Case Study: Spotify's cloud data infrastructure and analytics modernization.

Module 4: Data Transformation and Analytics Engineering
 

• SQL-based transformations and advanced query development.
• Implementing dbt models, sources, and transformations.
• Modular data pipelines and reusable transformation logic.
• Incremental models and dependency management.
• Documentation, testing, and data lineage implementation.
• Case Study: GitLab's analytics engineering practices and dbt-based data transformation workflows.

Module 5: Data Orchestration and Pipeline Automation
 

• Introduction to Apache Airflow and workflow orchestration.
• Designing automated ELT pipeline dependencies.
• Scheduling, monitoring, and managing data workflows.
• Error handling, retries, and pipeline recovery.
• Integrating orchestration tools with cloud data platforms.
• Case Study: Airbnb's data orchestration and large-scale analytical workflows.

Module 6: Data Quality, Testing, and Observability
 

• Implementing automated data validation frameworks.
• Data quality dimensions and testing methodologies.
• Managing schema changes and data anomalies.
• Data observability, freshness monitoring, and alerting.
• Establishing data reliability and service-level agreements.
• Case Study: Monte Carlo's data observability approach to enterprise data reliability.

Module 7: ELT Performance Optimization and Governance
 

• SQL query optimization and warehouse performance tuning.
• Managing cloud computing costs and resource utilization.
• Data governance, security, and access management.
• Implementing metadata catalogs and data lineage.
• Data privacy, compliance, and lifecycle management.
• Case Study: Capital One's cloud data governance and secure data architecture practices.

Module 8: Production Deployment and Modern ELT Implementation
 

• Building end-to-end production-ready ELT pipelines.
• Implementing Git-based version control and DevOps practices.
• Continuous integration and continuous deployment for data workflows.
• Integrating ELT pipelines with artificial intelligence and machine learning.
• Production monitoring, disaster recovery, and scalability.
• Case Study: LinkedIn's data infrastructure and large-scale production data engineering.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Hands-on ELT architecture design and implementation exercises.
• Practical SQL, dbt, and cloud data warehouse laboratories.
• Group projects involving end-to-end pipeline development.
• International industry case studies and collaborative discussions.
• Practical assessments, troubleshooting exercises, and knowledge evaluations.

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.