Cloud-Native Data Engineering Fundamentals Training Course

Course Introduction

The Cloud-Native Data Engineering Fundamentals Training Course provides a practical foundation for designing, building, and operating modern data engineering solutions using cloud-native technologies and architectural principles. Participants explore how cloud platforms transform traditional data pipelines through elastic infrastructure, managed services, automation, distributed processing, and scalable data storage.

The course introduces the core responsibilities of modern data engineers, including data ingestion, transformation, orchestration, storage, integration, quality management, monitoring, and secure data delivery. Participants learn how these capabilities work together within cloud environments to support analytics, business intelligence, machine learning, artificial intelligence, and operational data applications.

Participants will examine cloud-native data architectures based on data warehouses, data lakes, lakehouses, object storage, distributed processing engines, managed databases, streaming services, APIs, and event-driven systems. The training emphasizes architectural patterns that enable teams to build resilient and maintainable pipelines while taking advantage of cloud elasticity and managed infrastructure.

The course also addresses modern engineering practices such as infrastructure as code, automated testing, continuous integration and delivery, containerization, workflow orchestration, observability, version control, data contracts, and metadata management. Participants learn how engineering discipline can improve reliability, repeatability, deployment speed, operational visibility, and collaboration across data teams.

Practical attention is given to common cloud data engineering challenges, including data quality, schema evolution, incremental processing, change data capture, pipeline failures, security, access control, cost management, and workload optimization. Participants explore approaches for designing pipelines that can accommodate growing data volumes and changing business requirements without unnecessary operational complexity.

By completing this course, participants will be better prepared to contribute to cloud-native data engineering projects and understand the architecture and lifecycle of modern data pipelines. The training establishes a strong foundation for progressing into advanced cloud data engineering, analytics engineering, data platform development, streaming, machine learning engineering, and AI-oriented data infrastructure.

Duration

5 days

Who Should Attend

• Aspiring data engineers building foundational skills in modern cloud-based data engineering.

• Data engineers transitioning from traditional on-premises environments to cloud-native platforms.

• Database developers seeking practical knowledge of cloud data pipelines and distributed data processing.

• ETL developers modernizing traditional extraction, transformation, and loading workflows.

• Analytics engineers working with cloud data warehouses, transformation frameworks, and analytical datasets.

• Data analysts seeking a stronger understanding of the engineering processes behind analytical data platforms.

• Cloud engineers expanding their knowledge of data storage, pipelines, orchestration, and processing workloads.

• Software developers integrating applications and APIs with modern cloud data platforms.

• Business intelligence developers working with cloud-based analytical data environments.

• Solution architects supporting cloud data platform and analytics implementation projects.

• Technology professionals involved in cloud migration and data platform modernization initiatives.

• Project and technical managers overseeing cloud-native data engineering and analytics programs.

Course Objectives

• Understand fundamental cloud-native data engineering concepts and apply them to modern data ingestion, processing, storage, transformation, and delivery workflows.

• Design basic cloud data architectures using object storage, cloud databases, data warehouses, data lakes, lakehouses, and managed data processing services.

• Build reliable data pipelines that support batch and incremental processing while addressing dependencies, failures, retries, monitoring, and operational recovery.

• Apply ETL and ELT techniques to transform raw information into trusted datasets suitable for reporting, business intelligence, analytics, and downstream applications.

• Understand cloud-native orchestration principles and use workflow scheduling, dependency management, automation, and monitoring to coordinate data engineering processes.

• Implement data quality practices that address completeness, accuracy, consistency, uniqueness, validity, schema changes, and business rules throughout pipeline lifecycles.

• Apply cloud security fundamentals including identity management, access control, encryption, secrets management, network security, and appropriate data protection practices.

• Explore modern engineering practices including version control, infrastructure as code, automated testing, continuous integration, deployment automation, and reproducible environments.

• Optimize cloud data engineering workloads by considering scalability, distributed processing, resource utilization, storage design, query performance, and infrastructure costs.

• Develop a practical foundation for implementing maintainable cloud-native data platforms that can evolve to support streaming, machine learning, artificial intelligence, and advanced analytics.

Comprehensive Course Outline

Module 1: Foundations of Cloud-Native Data Engineering

• Understand the role of data engineering and how cloud-native principles change traditional approaches to data processing and platform development.

• Explore core data engineering lifecycle stages including ingestion, storage, transformation, orchestration, validation, serving, monitoring, and maintenance.

• Examine cloud computing characteristics such as elasticity, managed services, distributed infrastructure, automation, scalability, and consumption-based resource models.

• Compare traditional data engineering environments with cloud-native architectures and identify practical benefits, trade-offs, and implementation considerations.

Module 2: Cloud Data Storage and Platform Fundamentals

• Explore object storage, cloud databases, data warehouses, data lakes, lakehouses, and other storage technologies used in modern data architectures.

• Understand structured, semi-structured, and unstructured data storage requirements and how storage choices influence downstream engineering workflows.

• Examine file formats, partitioning, compression, metadata, schemas, and storage organization techniques for efficient analytical processing.

• Design basic cloud data platform architectures that separate ingestion, raw storage, transformation, curated datasets, and analytical consumption.

Module 3: Data Ingestion and Integration

• Design batch ingestion pipelines that reliably collect data from databases, files, applications, APIs, SaaS platforms, and external data sources.

• Explore incremental loading, change data capture, watermarking, deduplication, checkpointing, and other techniques for efficient data ingestion.

• Understand API-based integration, event-driven ingestion, message queues, connectors, and managed cloud services for connecting distributed data sources.

• Address common ingestion challenges including schema changes, source failures, duplicate records, late-arriving data, data validation, and retry strategies.

Module 4: Data Transformation and Processing

• Apply ETL and ELT principles to transform raw data into structured, validated, reusable datasets for analytics and downstream applications.

• Explore batch processing, distributed computing, SQL transformations, data preparation, joins, aggregations, filtering, enrichment, and business rule implementation.

• Understand transformation dependencies, incremental processing, reusable models, modular pipelines, and techniques for maintaining clean engineering workflows.

• Compare processing approaches according to data volume, latency requirements, complexity, infrastructure capabilities, and operational considerations.

Module 5: Workflow Orchestration and Pipeline Automation

• Understand workflow orchestration concepts including tasks, dependencies, schedules, retries, triggers, sensors, parameters, and failure handling.

• Design automated pipelines that coordinate ingestion, transformation, validation, publishing, notification, and downstream analytical processes.

• Explore dependency management, pipeline observability, logging, alerting, operational recovery, and scheduling strategies for reliable data workflows.

• Apply modular orchestration practices that improve maintainability, reusability, deployment consistency, and collaboration across data engineering teams.

Module 6: Data Quality, Testing, and Observability

• Establish data quality controls for completeness, accuracy, consistency, uniqueness, validity, referential integrity, freshness, and business-critical rules.

• Implement testing approaches for data transformations, schemas, pipeline outputs, dependencies, and critical analytical datasets.

• Explore data observability concepts including pipeline health, freshness monitoring, lineage, anomaly detection, operational metrics, and alerting.

• Develop systematic approaches for identifying, diagnosing, documenting, and resolving data quality and pipeline reliability problems.

Module 7: Cloud-Native Security and Governance

• Understand cloud identity and access management principles and apply least-privilege approaches to data engineering resources and workflows.

• Explore encryption, secrets management, network controls, secure service communication, data classification, and other fundamental cloud security practices.

• Examine metadata, lineage, ownership, retention, data contracts, and governance practices that support trusted and controlled cloud data environments.

• Design data engineering workflows that incorporate security and governance requirements without unnecessarily restricting legitimate analytical access.

Module 8: DevOps and Engineering Practices for Data

• Apply version control practices to data pipeline code, transformation logic, configuration, documentation, and infrastructure definitions.

• Explore infrastructure as code, containerization, automated deployment, continuous integration, and continuous delivery for cloud data engineering environments.

• Implement automated testing and deployment workflows that improve reproducibility, reduce manual errors, and support controlled platform changes.

• Examine development, testing, staging, and production environment strategies for maintaining consistent and reliable data engineering deployments.

Module 9: Performance, Scalability, and Cloud Cost Optimization

• Understand how data volume, query patterns, storage design, processing architecture, and workload concurrency influence cloud data engineering performance.

• Apply partitioning, clustering, compression, caching, parallel processing, incremental processing, and workload management techniques where appropriate.

• Explore cloud cost drivers including compute consumption, storage, data transfer, managed services, orchestration, and inefficient processing patterns.

• Develop practical monitoring and optimization approaches that balance pipeline performance, scalability, reliability, resource utilization, and operational cost.

Module 10: Modern Cloud Data Engineering and Emerging Practices

• Explore streaming data engineering, event-driven architectures, real-time pipelines, and architectures designed for continuously changing information.

• Examine lakehouse engineering, data products, Data Mesh concepts, semantic layers, and other emerging approaches influencing cloud-native data platforms.

• Investigate AI-ready data engineering requirements involving feature pipelines, embeddings, vector data, retrieval systems, and machine learning workflows.

• Develop practical next-step strategies for extending foundational cloud engineering capabilities toward advanced analytics, AI, streaming, and enterprise data platforms.

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 a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, 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.