Organizations face continuous challenges in ingesting, integrating, transforming, and delivering data in real time for analytics and decision-making. This course provides participants with a practical and project-oriented approach to mastering applied data engineering, focusing on real-world techniques for managing ingestion, integration, and production pipelines.
Participants will explore the end-to-end lifecycle of modern data engineering, beginning with raw data ingestion from diverse sources, through integration across structured and unstructured systems, to production deployment of robust and scalable pipelines. By applying industry-standard tools and frameworks, learners will gain the technical expertise to handle complex data flows, manage transformations, and ensure reliability in high-demand production environments.
The course bridges theory with practice by leveraging case studies and project simulations drawn from enterprise environments. Learners will not only understand the principles of data engineering but will also build hands-on skills in handling streaming and batch data, orchestrating workflows, and automating pipeline deployment across hybrid and cloud infrastructures.
In addition, emerging topics such as real-time event processing, cloud-native architectures, and data observability will be explored to help participants align with the latest industry standards. Emphasis will be placed on ensuring data quality, governance, and compliance within engineered pipelines crucial for supporting analytics, AI, and machine learning applications.
Through collaborative exercises and guided projects, participants will apply what they learn to design, develop, and optimize data engineering solutions that can withstand the complexity and scale of modern digital enterprises. By the end of the course, learners will have both the theoretical foundation and the applied project experience necessary to become effective data engineering professionals.
Ultimately, this program is designed to transform learners into practitioners capable of translating business requirements into production-ready data pipelines that enable organizational intelligence, scalability, and long-term innovation.
Who Should Attend
- Data Engineers seeking to enhance their pipeline design and integration skills.
- Database Administrators transitioning to modern data engineering roles.
- Software Engineers and Developers working with big data applications.
- Data Analysts aspiring to shift into data engineering roles.
- IT Professionals responsible for data infrastructure and system integration.
- Cloud Engineers managing data platforms across AWS, Azure, or GCP.
- Business Intelligence (BI) specialists supporting enterprise reporting systems.
- Machine Learning Engineers requiring reliable data pipelines for model deployment.
- Project Managers overseeing data engineering or analytics projects.
- Technical Consultants providing enterprise data solutions.
Duration
10 days
Course Objectives
- Develop a strong understanding of modern data engineering concepts, frameworks, and methodologies.
- Learn how to ingest structured, semi-structured, and unstructured data from multiple sources.
- Gain expertise in designing data integration workflows for batch and real-time systems.
- Acquire hands-on skills in building and maintaining production-ready pipelines.
- Apply orchestration tools such as Airflow, Luigi, or Prefect for workflow automation.
- Implement cloud-native pipelines across AWS, Azure, and GCP environments.
- Ensure data quality, governance, and compliance throughout pipeline development.
- Manage challenges of scalability, latency, and fault tolerance in production systems.
- Explore techniques for monitoring, logging, and troubleshooting data pipelines.
- Build practical experience through project-based case studies and simulations.
- Understand the role of pipelines in enabling advanced analytics and AI applications.
- Cultivate the ability to translate business requirements into efficient engineering solutions.
Comprehensive Course Outline
Module 1: Foundations of Data Engineering
- Introduction to Data Engineering Concepts and Practices
- The Role of Pipelines in Analytics and AI
- Overview of Batch vs. Streaming Data
- Tools, Frameworks, and Industry Landscape
Module 2: Data Ingestion Techniques
- Collecting Data from APIs, Databases, and Files
- Handling Streaming Sources (Kafka, Kinesis, Pulsar)
- Batch Ingestion from Legacy Systems
- Best Practices for Secure and Scalable Ingestion
Module 3: Data Integration and Transformation
- ETL vs. ELT Approaches
- Data Cleaning, Standardization, and Enrichment
- Schema Mapping and Metadata Management
- Building Reusable Integration Workflows
Module 4: Storage Systems for Engineering Pipelines
- Relational vs. NoSQL Databases in Pipelines
- Data Lakes and Lakehouse Architectures
- Distributed Storage with HDFS and Cloud Object Stores
- Choosing the Right Storage for Use Cases
Module 5: Workflow Orchestration
- Principles of Workflow Automation
- Introduction to Apache Airflow, Luigi, and Prefect
- DAGs and Scheduling Strategies
- Error Handling and Retry Mechanisms
Module 6: Real-Time Data Processing
- Introduction to Event-Driven Architectures
- Stream Processing with Spark, Flink, and Beam
- Event Streaming with Kafka and Kinesis
- Designing Low-Latency Data Flows
Module 7: Batch Data Processing
- Building Scalable ETL Pipelines with Spark
- Scheduling Batch Jobs in Cloud Environments
- Data Aggregation and Historical Analysis
- Managing Resource Efficiency in Batch Systems
Module 8: Cloud-Native Data Engineering
- Cloud Architectures for Data Pipelines
- AWS Glue, Dataflow, and Synapse Pipelines
- Hybrid and Multi-Cloud Considerations
- Cost Optimization for Cloud Data Pipelines
Module 9: Data Governance and Quality
- Ensuring Data Lineage and Traceability
- Master Data Management Practices
- Data Quality Metrics and Validation
- Compliance with GDPR, HIPAA, and Industry Standards
Module 10: Data Pipeline Monitoring and Observability
- Metrics, Logging, and Monitoring Best Practices
- Tools for Observability (Prometheus, Grafana, ELK)
- Detecting Anomalies and Failures in Pipelines
- Implementing Alerting and Escalation Policies
Module 11: Security in Data Engineering
- Access Control and Authentication in Pipelines
- Data Encryption in Transit and at Rest
- Managing Secrets and Credentials
- Security Best Practices for Cloud and On-Prem Systems
Module 12: Scaling and Optimizing Pipelines
- Strategies for Horizontal and Vertical Scaling
- Performance Tuning for Batch and Stream Workloads
- Caching and Storage Optimization Techniques
- High Availability and Fault Tolerance
Module 13: Advanced Integration Patterns
- Microservices and API-Driven Integration
- Using Message Queues for Decoupling Pipelines
- Federated and Virtualized Data Integration
- Orchestration Across Hybrid Systems
Module 14: Applied Case Studies
- Building a Customer 360 Pipeline
- Real-Time Fraud Detection Pipeline
- IoT Data Processing and Analytics
- Data Engineering for Machine Learning Models
Module 15: Project – Designing End-to-End Pipelines
- Defining Business Requirements and Data Sources
- Designing Scalable Ingestion and Integration Flows
- Deploying a Production-Ready Data Pipeline
- Presenting and Documenting Solutions
Module 16: Future Trends and Emerging Issues
- Data Mesh and Data Fabric Architectures
- Automation and AI in Data Engineering
- Rise of Serverless Data Pipelines
- Sustainability and Green Data Engineering
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
