Analytics Engineering for Modern Data Platforms Training Course

 Analytics Engineering for Modern Data Platforms Training Course 

Introduction 

Analytics Engineering for Modern Data Platforms Training Course provides comprehensive practical knowledge and technical expertise in designing, developing, managing, and optimizing modern data platforms for enterprise analytics. The course explores the evolving role of analytics engineering in transforming raw data into reliable, structured, and business-ready information through modern data architecture, data transformation, cloud data warehousing, data modeling, and automated data pipelines. Participants gain practical experience with advanced analytics engineering tools and technologies, including dbt, SQL, Python, Snowflake, Google BigQuery, Amazon Redshift, Databricks, and Apache Airflow. Emphasis is placed on scalable data infrastructure, cloud-native analytics, data quality assurance, metadata management, and modern data stack implementation. 

The training also examines emerging industry practices such as DataOps, analytics automation, data observability, semantic modeling, artificial intelligence-driven analytics, and self-service business intelligence. Through practical exercises and international case studies, participants learn how to build dependable analytical datasets, implement version-controlled transformation workflows, optimize query performance, and establish governance frameworks for modern enterprise data environments. The course equips professionals with the competencies required to support data-driven decision-making, digital transformation, real-time analytics, and enterprise intelligence initiatives across diverse industries. 

Course Objectives 

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

1. Understand modern analytics engineering principles and data platform architectures.
2. Design scalable cloud-native data platforms and infrastructure.
3. Develop advanced SQL-based data transformation workflows.
4. Implement dbt for analytics engineering automation.
5. Apply dimensional modeling and enterprise data modeling techniques.
6. Build automated and scalable data pipelines.
7. Integrate Snowflake, BigQuery, and Databricks environments.
8. Implement data quality testing and validation frameworks.
9. Apply DataOps, CI/CD, and version control practices.
10. Optimize analytical query performance and storage efficiency.
11. Implement data governance, security, and metadata management.
12. Develop reusable semantic layers and business-ready datasets.
13. Apply data observability and monitoring for reliable analytics.

Organizational Benefits
 

1. Improved enterprise data management and operational efficiency.
2. Faster and more reliable business intelligence delivery.
3. Reduced data processing errors and inconsistencies.
4. Enhanced cloud data platform scalability.
5. Improved data governance and regulatory compliance.
6. Reduced analytics engineering and infrastructure costs.
7. Stronger collaboration between data engineering and business teams.
8. Increased automation of analytical workflows.
9. Better strategic decision-making through trusted data.
10. Improved organizational competitiveness through advanced analytics.

Target Audiences
 

1. Analytics Engineers and Data Engineers.
2. Data Scientists and Data Analysts.
3. Business Intelligence Developers.
4. Database Administrators and Architects.
5. Cloud Data Platform Engineers.
6. Enterprise Data Architects.
7. IT Managers and Digital Transformation Specialists.
8. Business Intelligence and Data Governance Professionals.

Course Duration: 5 days
 
Course Modules

Module 1: Foundations of Modern Analytics Engineering
 

• Analytics engineering concepts, responsibilities, and industry trends.
• Modern data stack architecture and enterprise data ecosystems.
• Data engineering versus analytics engineering workflows.
• Cloud-native data platforms and distributed computing.
• Data lifecycle management and analytical architecture.
• Case study: Netflix's data-driven analytics infrastructure.

Module 2: Advanced SQL and Data Transformation
 

• Advanced SQL queries, joins, window functions, and CTEs.
• Data cleansing, normalization, and transformation techniques.
• Incremental data processing and transformation strategies.
• Reusable SQL models and transformation frameworks.
• Query optimization and analytical performance.
• Case study: Airbnb's SQL-based analytics engineering practices.

Module 3: dbt and Analytics Engineering Automation
 

• Introduction to dbt Core and dbt Cloud.
• Developing modular, reusable, and scalable dbt models.
• Implementing dbt testing, documentation, and dependencies.
• Managing analytics workflows using Jinja and macros.
• Version control and automated deployment pipelines.
• Case study: GitLab's modern analytics engineering workflows.

Module 4: Modern Data Warehousing and Cloud Platforms
 

• Cloud data warehouse architecture and implementation.
• Snowflake, Google BigQuery, and Amazon Redshift.
• Data lakes, lakehouses, and cloud storage integration.
• Warehouse partitioning and workload optimization.
• Data ingestion and cross-platform integration.
• Case study: Spotify's cloud-based data infrastructure.

Module 5: Data Modeling and Semantic Layer Development
 

• Dimensional modeling and star schema architecture.
• Fact tables, dimension tables, and data relationships.
• Enterprise data models and reusable analytical datasets.
• Semantic layers, metrics definitions, and business logic.
• Data cataloguing and metadata management.
• Case study: Uber's enterprise data modeling practices.

Module 6: Data Quality, Testing, and Observability
 

• Automated data quality testing and validation.
• Implementing dbt tests and data integrity checks.
• Data observability, lineage, and anomaly detection.
• Monitoring pipeline failures and data freshness.
• Data reliability frameworks and incident management.
• Case study: Monte Carlo's data observability practices in enterprise environments.

Module 7: DataOps, CI/CD, and Workflow Orchestration
 

• DataOps principles and analytics workflow automation.
• Git-based collaboration and version control.
• CI/CD implementation for analytics engineering.
• Apache Airflow and automated pipeline orchestration.
• Deployment management and continuous integration.
• Case study: LinkedIn's large-scale data pipeline management.

Module 8: Data Governance, Security, and Platform Optimization
 

• Enterprise data governance and stewardship frameworks.
• Data access control, privacy, and security management.
• Regulatory compliance and responsible data management.
• Platform cost optimization and resource management.
• Performance monitoring and scalable analytics architecture.
• Case study: Capital One's cloud data governance and security practices.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using modern analytics engineering tools.
• Hands-on SQL, dbt, and cloud data platform exercises.
• Real-world international case studies and group assignments.
• Collaborative data modeling and pipeline development projects.
• Practical assessments, knowledge checks, and implementation workshops.

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.