Advanced Data Engineering and Pipeline Development Training Course

 Advanced Data Engineering and Pipeline Development Training Course 

Introduction 

Advanced Data Engineering and Pipeline Development Training Course is designed to equip professionals with advanced technical competencies in modern data engineering, enterprise data architecture, big data processing, and scalable data pipeline development. The course explores emerging technologies and industry best practices in data ingestion, data integration, data transformation, data orchestration, cloud data engineering, distributed computing, and real-time analytics. Participants will develop practical expertise in building reliable, scalable, secure, and high-performance data pipelines using modern tools and frameworks such as Apache Spark, Apache Kafka, Apache Airflow, Python, SQL, Databricks, and cloud-based data platforms. Emphasis is placed on automated workflows, data quality management, data governance, metadata management, and DataOps to support enterprise-wide digital transformation and data-driven decision-making. 

The training also examines advanced data pipeline optimization, lakehouse architecture, data warehouse modernization, streaming analytics, and machine learning data infrastructure. Through practical exercises and international case studies, participants will explore how leading organizations leverage automated data engineering solutions to manage complex datasets, improve operational efficiency, and generate actionable business intelligence. The programme integrates contemporary approaches to cloud-native engineering, infrastructure automation, containerization, continuous integration and continuous delivery, and scalable distributed data processing. Participants will gain the skills required to design, implement, monitor, troubleshoot, and maintain production-ready data engineering solutions that support artificial intelligence, business analytics, predictive modelling, and enterprise data management initiatives. 

Course Objectives 

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

1. Design scalable enterprise data engineering architectures.
2. Develop advanced automated data pipelines and workflows.
3. Implement efficient Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes.
4. Apply distributed computing and big data processing techniques.
5. Develop real-time streaming pipelines using modern frameworks.
6. Implement cloud-native data engineering solutions.
7. Optimize data storage, processing, and query performance.
8. Apply DataOps and automated pipeline orchestration.
9. Implement advanced data quality and validation frameworks.
10. Strengthen data governance, security, and regulatory compliance.
11. Integrate data lakes, warehouses, and lakehouse platforms.
12. Deploy containerized and production-ready data engineering applications.
13. Implement continuous integration and continuous delivery for data pipelines.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced data processing costs and operational inefficiencies.
3. Enhanced business intelligence and analytics capabilities.
4. Increased reliability of automated data workflows.
5. Improved data security and regulatory compliance.
6. Faster access to real-time business insights.
7. Strengthened artificial intelligence and machine learning infrastructure.
8. Better enterprise data governance and management.
9. Improved scalability of data processing systems.
10. Increased organizational competitiveness through data-driven innovation.

Target Audiences
 

1. Data engineers and data architects.
2. Database administrators and developers.
3. Big data and cloud computing professionals.
4. Business intelligence and analytics specialists.
5. Software engineers and application developers.
6. Information technology managers and consultants.
7. Artificial intelligence and machine learning engineers.
8. Data scientists and enterprise technology professionals.

Course Duration: 10 days

Course Modules

Module 1: Advanced Data Engineering Fundamentals
 

• Enterprise data engineering principles and architecture.
• Modern data engineering lifecycle and workflows.
• Data integration and interoperability frameworks.
• Scalable data infrastructure design.
• Data engineering trends and emerging technologies.
• Case study: Netflix's large-scale data engineering ecosystem.

Module 2: Advanced Data Ingestion and Integration
 

• Batch and incremental data ingestion techniques.
• API-based and database data integration.
• Change Data Capture (CDC) implementation.
• Data extraction from structured and unstructured sources.
• Automated ingestion and error handling.
• Case study: Uber's real-time data ingestion architecture.

Module 3: Advanced ETL and ELT Pipeline Development
 

• Designing enterprise ETL and ELT workflows.
• Advanced data transformation using Python and SQL.
• Data cleansing and normalization techniques.
• Incremental loading and pipeline optimization.
• Pipeline testing and validation frameworks.
• Case study: Airbnb's data transformation infrastructure.

Module 4: Distributed Data Processing
 

• Distributed computing architecture and principles.
• Apache Spark processing frameworks.
• Parallel processing and workload management.
• Partitioning, caching, and performance optimization.
• Fault tolerance and distributed data storage.
• Case study: Netflix's distributed data processing applications.

Module 5: Real-Time Data Streaming Architecture
 

• Event-driven data engineering architecture.
• Apache Kafka and stream processing fundamentals.
• Real-time data ingestion and transformation.
• Stream processing and event management.
• Monitoring streaming performance and reliability.
• Case study: LinkedIn's Kafka-based streaming ecosystem.

Module 6: Advanced Workflow Orchestration
 

• Data pipeline orchestration principles.
• Apache Airflow DAG development.
• Scheduling and dependency management.
• Workflow automation and failure recovery.
• Pipeline monitoring and execution management.
• Case study: Airbnb's adoption of Apache Airflow.

Module 7: Cloud Data Engineering Platforms
 

• Cloud-native data engineering architecture.
• Amazon Web Services, Microsoft Azure, and Google Cloud.
• Cloud data storage and processing services.
• Serverless data pipeline development.
• Cloud resource optimization and scalability.
• Case study: Spotify's cloud-based data infrastructure.

Module 8: Data Lakes, Warehouses and Lakehouse Architecture
 

• Enterprise data lake architecture.
• Modern cloud data warehouse development.
• Lakehouse architecture and implementation.
• Delta Lake and Apache Iceberg fundamentals.
• Data cataloguing and metadata management.
• Case study: Databricks lakehouse implementations.

Module 9: Advanced Database Engineering and Optimization
 

• Relational and NoSQL database architecture.
• Advanced SQL query optimization.
• Indexing, partitioning, and data modelling.
• Database performance monitoring.
• Distributed database management.
• Case study: Amazon's large-scale database infrastructure.

Module 10: Data Quality, Testing and Observability
 

• Enterprise data quality management frameworks.
• Automated data validation and testing.
• Data lineage and observability.
• Anomaly detection and pipeline troubleshooting.
• Data reliability and service-level agreements.
• Case study: Monte Carlo's data observability applications.

Module 11: DataOps and Pipeline Automation
 

• DataOps principles and implementation.
• Continuous integration and continuous delivery.
• Automated pipeline deployment and testing.
• Version control and collaborative development.
• Infrastructure as Code and configuration management.
• Case study: Google's automated data engineering practices.

Module 12: Containerization and Pipeline Deployment
 

• Docker containerization for data applications.
• Kubernetes orchestration fundamentals.
• Microservices-based data engineering.
• Deployment automation and environment management.
• Production monitoring and resource allocation.
• Case study: Spotify's containerized engineering infrastructure.

Module 13: Data Security, Governance and Compliance
 

• Enterprise data governance frameworks.
• Data encryption and access control.
• Privacy-preserving data engineering.
• Metadata management and data lineage.
• Regulatory compliance and risk management.
• Case study: Financial sector data governance practices.

Module 14: AI-Ready Data Infrastructure and Advanced Analytics
 

• Data engineering for artificial intelligence.
• Machine learning data pipeline development.
• Feature engineering and feature stores.
• Vector databases and retrieval-augmented generation data workflows.
• Model data preparation and pipeline automation.
• Case study: Uber's Michelangelo machine learning platform.

Module 15: Capstone Project and Enterprise Pipeline Implementation
 

• Designing an end-to-end enterprise data pipeline.
• Integrating batch and real-time processing.
• Implementing automated data transformation.
• Pipeline deployment, testing, and performance evaluation.
• Developing technical documentation and operational dashboards.
• Case study: Building a scalable data platform inspired by Netflix's engineering architecture.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical exercises and hands-on pipeline development.
• Real-world enterprise data engineering simulations.
• Group discussions and international case study analysis.
• Collaborative capstone projects and implementation exercises.
• Practical assessments, troubleshooting, 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.