Data Engineering and Cloud Architecture Course: Building Scalable Big Data Solutions

Introduction

The rise of big data and cloud technologies has transformed how organizations store, process, and analyze information at scale. Modern enterprises need scalable, secure, and efficient data infrastructures to unlock insights, optimize operations, and sustain innovation. This course provides a comprehensive framework for mastering data engineering and cloud architecture, equipping professionals with the knowledge and skills to design and deploy enterprise-grade data solutions.

The course begins by introducing participants to the fundamentals of data engineering covering pipelines, ETL processes, and data integration strategies before advancing into cloud-native architectures. Learners will explore how leading cloud platforms such as AWS, Microsoft Azure, and Google Cloud enable the development of high-performing, cost-optimized data ecosystems.

Hands-on sessions will emphasize the practical aspects of building data pipelines, managing storage systems, and applying distributed computing frameworks like Apache Spark, Hadoop, and Databricks. Participants will learn to handle structured, semi-structured, and unstructured data while ensuring performance, reliability, and governance.

Special attention will be given to cloud-based scalability and elasticity, enabling learners to design architectures that grow with organizational needs. Topics such as serverless computing, containerization with Docker and Kubernetes, and hybrid cloud solutions will be examined to ensure adaptability across diverse business contexts.

Security, compliance, and resilience are central themes of the course. Participants will gain exposure to encryption, identity and access management, governance frameworks, and disaster recovery strategies that safeguard sensitive enterprise data and ensure business continuity.

By the end of the course, learners will have the technical depth and strategic insight to design, build, and manage scalable big data solutions in the cloud. They will be prepared to lead data-driven digital transformation initiatives and deliver architectures that are robust, secure, and future-ready.

Who Should Attend

• Data engineers seeking to expand their expertise into cloud-based solutions.
• Cloud architects responsible for designing enterprise-level data infrastructures.
• IT managers overseeing big data projects and cloud migrations.
• Business intelligence and analytics professionals moving into cloud ecosystems.
• Software engineers transitioning into data engineering roles.
• Data scientists requiring scalable data pipelines for advanced modeling.
• System administrators and DevOps professionals integrating cloud data systems.
• Consultants advising organizations on cloud adoption and big data solutions.
• Project managers involved in digital transformation and data initiatives.
• Risk and compliance officers ensuring governance in data architecture.
• Researchers and academics exploring cloud-native data engineering methods.
• Senior executives seeking to leverage cloud and data engineering for growth.

Duration

10 days

Course Objectives

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

• Understand the foundations and principles of data engineering and cloud architecture.
• Design and implement scalable data pipelines for structured and unstructured data.
• Deploy distributed computing frameworks such as Spark, Hadoop, and Databricks.
• Architect enterprise-grade solutions on AWS, Azure, and Google Cloud.
• Optimize cloud data storage for performance, cost-efficiency, and scalability.
• Apply ETL and ELT workflows in modern data ecosystems.
• Implement governance, security, and compliance in cloud data solutions.
• Leverage containerization and orchestration tools like Docker and Kubernetes.
• Integrate serverless computing and microservices into data pipelines.
• Build real-time data processing and streaming solutions for analytics.
• Evaluate cloud strategies for hybrid, multi-cloud, and on-premises integration.
• Anticipate future trends in data engineering, AI integration, and edge computing.

Comprehensive Course Outline

Module 1: Introduction to Data Engineering and Cloud Architecture

• Fundamentals of data engineering in enterprise ecosystems.
• Overview of cloud platforms and service models (IaaS, PaaS, SaaS).
• The role of data architecture in digital transformation.
• Case studies of cloud-enabled data-driven organizations.

Module 2: Data Ingestion and Integration

• ETL and ELT frameworks for modern data pipelines.
• APIs, batch processing, and streaming ingestion methods.
• Integration of structured, semi-structured, and unstructured data.
• Automation and workflow orchestration tools.

Module 3: Cloud Data Storage Systems

• Cloud storage options: object, block, and file storage.
• Relational vs. non-relational databases in the cloud.
• Data lakes vs. data warehouses: design considerations.
• Scalability and cost optimization strategies.

Module 4: Distributed Computing and Big Data Frameworks

• Apache Hadoop and MapReduce fundamentals.
• Apache Spark for large-scale analytics.
• Databricks unified data platform.
• Real-world applications of distributed computing.

Module 5: Cloud Platforms for Data Engineering

• Building data solutions on AWS (S3, Redshift, EMR, Glue).
• Microsoft Azure Synapse, Data Factory, and HDInsight.
• Google Cloud BigQuery, Dataflow, and Dataproc.
• Comparative analysis and multi-cloud strategies.

Module 6: Data Pipeline Design and Orchestration

• Designing fault-tolerant data pipelines.
• Orchestration with Apache Airflow and Prefect.
• Event-driven pipeline architecture.
• Monitoring, logging, and pipeline optimization.

Module 7: Real-Time Data Processing

• Event streaming with Kafka and Kinesis.
• Real-time analytics using Spark Streaming and Flink.
• Architectures for low-latency data delivery.
• Use cases: fraud detection, IoT, and personalization.

Module 8: Security and Governance in Cloud Architectures

• Data encryption at rest and in transit.
• Identity and access management frameworks.
• Regulatory compliance (GDPR, HIPAA, SOC 2).
• Building resilient and auditable data systems.

Module 9: Containerization and Microservices

• Containerization with Docker for data solutions.
• Kubernetes orchestration and scaling.
• Microservices in data engineering workflows.
• Case studies of containerized cloud deployments.

Module 10: Serverless Computing and Data Workflows

• Serverless functions for scalable processing.
• Cloud-native serverless frameworks (AWS Lambda, Azure Functions).
• Event-driven serverless data architectures.
• Cost and performance optimization.

Module 11: Data Visualization and Business Intelligence Integration

• Building dashboards with Power BI and Tableau.
• Cloud-native visualization solutions.
• Storytelling with big data insights.
• Integrating predictive analytics with BI tools.

Module 12: Hybrid and Multi-Cloud Strategies

• Benefits and challenges of multi-cloud deployments.
• Hybrid solutions integrating on-premises systems.
• Cloud migration frameworks and methodologies.
• Disaster recovery and business continuity planning.

Module 13: Advanced Analytics and Machine Learning in the Cloud

• Integrating ML pipelines with cloud architectures.
• AI-powered big data solutions.
• AutoML tools for cloud-native environments.
• Scalable training and deployment of models.

Module 14: Performance Optimization and Cost Management

• Monitoring and optimizing data pipelines.
• Cost-saving strategies in cloud data storage.
• Auto-scaling and elasticity in cloud systems.
• Benchmarking and performance testing.

Module 15: Industry-Specific Cloud Data Solutions

• Financial services: risk and fraud detection.
• Healthcare: data security and predictive analytics.
• Retail: customer insights and personalization.
• Manufacturing: IoT and predictive maintenance.

Module 16: Project and Emerging Trends

• End-to-end project: designing a scalable cloud-based solution.
• Future of data engineering: quantum computing and edge analytics.
• Ethical implications of AI and cloud-based decision systems.
• Preparing enterprises for cloud-native data innovation.

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