Course Introduction
Data engineering plays a critical role in modern enterprises by enabling organizations to collect, transform, and deliver data efficiently for analytics and decision-making. As businesses adopt advanced digital systems, the ability to design scalable, automated pipelines becomes vital for competitiveness.
This course provides participants with in-depth knowledge of data engineering principles, tools, and frameworks. It emphasizes building reliable pipelines that move and process structured, semi-structured, and unstructured data at scale.
Learners will explore pipeline automation, workflow orchestration, and real-time data processing. Through hands-on labs, participants will design ETL/ELT processes, implement streaming solutions, and deploy workflows using industry-standard platforms such as Apache Airflow, Spark, and Kafka.
The course also highlights emerging trends such as serverless data engineering, cloud-native pipelines, and the integration of AI-driven tools for pipeline monitoring, anomaly detection, and optimization.
Participants will gain practical experience in designing fault-tolerant, secure, and compliant pipelines across multi-cloud and hybrid environments, with emphasis on governance, scalability, and cost-efficiency.
By the end of this program, learners will be equipped to build and automate data pipelines that support advanced analytics, machine learning models, and data-driven decision-making across industries.
Who Should Attend
- Data engineers and architects seeking expertise in pipeline automation.
- Data scientists and analysts requiring reliable pipelines for advanced analytics.
- IT professionals and system administrators managing enterprise data flows.
- Cloud engineers and DevOps specialists focusing on automated data delivery.
- Software developers working on real-time and batch data processing systems.
- Business intelligence professionals integrating data for dashboards and reporting.
- Compliance and governance officers overseeing secure and regulated data pipelines.
- Database administrators aiming to modernize data storage and movement.
- Consultants delivering scalable data solutions for enterprises.
- Project managers leading data transformation initiatives.
- Researchers exploring data engineering methods for large-scale studies.
Duration
10 days
Course Objectives
- Provide an in-depth understanding of modern data engineering practices, tools, and frameworks for scalable pipelines.
- Equip learners with skills to design, build, and automate ETL/ELT pipelines for real-time and batch data processing.
- Develop expertise in workflow orchestration with tools such as Apache Airflow, Luigi, and Prefect for pipeline automation.
- Train participants in cloud-native and serverless data engineering solutions for hybrid and multi-cloud deployments.
- Enhance knowledge of real-time data streaming technologies like Apache Kafka and Spark Structured Streaming.
- Build capacity in monitoring, logging, and optimizing pipelines for performance, fault tolerance, and cost efficiency.
- Strengthen understanding of data governance, compliance, and security in automated data workflows.
- Foster skills in integrating data pipelines with analytics, BI dashboards, and machine learning platforms.
- Expose learners to automation strategies for data validation, quality control, and anomaly detection.
- Cultivate problem-solving through hands-on labs and case studies on enterprise data engineering challenges.
- Encourage innovation by exploring AI-powered pipeline monitoring and optimization solutions.
- Prepare participants to lead organizational data modernization and transformation projects.
Course Outline
Module 1: Foundations of Data Engineering
- Role of data engineering in modern enterprises.
- Key components of data pipelines.
- ETL vs ELT processes.
- Core data engineering tools and platforms.
Module 2: Data Modeling and Storage
- Relational and non-relational data models.
- Designing schemas for analytics.
- Data lakes, warehouses, and lakehouses.
- Best practices for scalable storage.
Module 3: ETL and ELT Processes
- Extracting, transforming, and loading data.
- Automating ETL workflows.
- Tools for ETL/ELT automation.
- Common challenges and solutions.
Module 4: Workflow Orchestration
- Introduction to Apache Airflow, Luigi, and Prefect.
- Designing DAGs for pipeline management.
- Scheduling and dependency handling.
- Case study on orchestration in production.
Module 5: Data Streaming and Real-Time Processing
- Fundamentals of real-time data streams.
- Apache Kafka and Spark Structured Streaming.
- Building event-driven pipelines.
- Use cases in finance, IoT, and retail.
Module 6: Cloud Data Engineering
- Cloud-native architectures for pipelines.
- Serverless pipeline solutions.
- Multi-cloud pipeline integration.
- Cost management in cloud pipelines.
Module 7: Data Governance and Compliance
- Principles of data governance in pipelines.
- Compliance with GDPR, HIPAA, and industry standards.
- Secure data handling practices.
- Auditing and monitoring for compliance.
Module 8: Automation and Optimization
- Automating data validation and quality checks.
- AI-driven anomaly detection in pipelines.
- Pipeline performance tuning.
- Continuous integration and deployment of pipelines.
Module 9: Big Data and Distributed Systems
- Hadoop ecosystem and Spark fundamentals.
- Distributed computing concepts.
- Handling petabyte-scale data.
- Challenges of distributed pipelines.
Module 10: Data Integration and APIs
- API-driven data collection and integration.
- Connecting pipelines with external systems.
- Managing structured and unstructured sources.
- Microservices and pipeline integration.
Module 11: Monitoring and Reliability
- Tools for monitoring pipelines in real time.
- Logging and alerting systems.
- Fault-tolerant pipeline design.
- Ensuring high availability and reliability.
Module 12: Machine Learning and AI Integration
- Feeding ML models with automated pipelines.
- Model deployment and monitoring in production.
- Data preparation for AI workflows.
- Case studies in ML-driven pipelines.
Module 13: Business Intelligence and Visualization
- Connecting pipelines to BI tools like Power BI and Tableau.
- Building real-time dashboards.
- Automating report generation.
- Enabling data democratization across organizations.
Module 14: Security in Data Pipelines
- Encrypting data in transit and at rest.
- Access control and identity management.
- Securing APIs and integration points.
- Threat detection in data workflows.
Module 15: Case Studies and Industry Applications
- Retail and e-commerce pipeline automation.
- Financial services and fraud detection.
- Healthcare data pipelines for analytics.
- IoT and smart city applications.
Module 16: Project and Future Trends
- Designing a complete automated pipeline.
- Implementing monitoring and optimization.
- Presenting pipeline solutions to stakeholders.
- Future trends in AI-driven and cloud-native 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.
