Cloud-Native Data Platform Engineering Training Course

 Cloud-Native Data Platform Engineering Training Course 

Introduction 

Cloud-Native Data Platform Engineering Training Course is designed to equip professionals with the technical expertise required to design, develop, deploy, and manage scalable, resilient, and high-performance data platforms using modern cloud technologies. As organizations increasingly adopt digital transformation, artificial intelligence, machine learning, big data analytics, and real-time data processing, cloud-native architecture has become essential for building flexible and cost-effective data infrastructure. This course explores cloud-native data engineering, distributed computing, containerization, Kubernetes orchestration, microservices architecture, data lakes, lakehouse solutions, data warehousing, event-driven architecture, and automated data pipelines. Participants gain practical knowledge of AWS, Microsoft Azure, Google Cloud Platform, Apache Spark, Apache Kafka, Docker, Kubernetes, Terraform, and modern data platform engineering practices. 

The programme emphasizes enterprise data architecture, cloud infrastructure automation, DataOps, DevOps integration, data governance, platform observability, and continuous integration and continuous delivery (CI/CD). Through practical exercises and international case studies, participants learn to build secure, interoperable, and scalable data ecosystems that support real-time analytics, enterprise reporting, intelligent applications, and data-driven decision-making. The course also examines cloud cost optimization, serverless computing, data security, infrastructure as code, and platform reliability engineering. By integrating industry best practices and emerging cloud-native technologies, participants develop the capabilities to modernize legacy data environments, accelerate data product delivery, improve operational efficiency, and establish future-ready enterprise data platforms. 

Course Objectives 

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

1. Design scalable cloud-native data platform architectures using modern cloud computing technologies.
2. Implement containerization and Kubernetes orchestration for distributed data workloads.
3. Develop automated data pipelines using modern data engineering frameworks.
4. Integrate cloud data lakes, lakehouse architectures, and enterprise data warehouses.
5. Apply microservices and event-driven architecture to data platform development.
6. Implement infrastructure as code using Terraform and cloud automation tools.
7. Configure CI/CD pipelines for continuous data platform deployment.
8. Apply DataOps and DevOps practices to improve data engineering workflows.
9. Integrate Apache Kafka and Apache Spark for real-time data processing.
10. Implement enterprise data governance, security, and access management.
11. Optimize cloud infrastructure performance, scalability, and operational costs.
12. Establish platform observability, monitoring, and reliability engineering practices.
13. Develop resilient cloud-native platforms supporting artificial intelligence, machine learning, and advanced analytics.

Organizational Benefits
 

1. Improved scalability and flexibility of enterprise data infrastructure.
2. Reduced cloud infrastructure and data processing operational costs.
3. Faster deployment of enterprise data engineering solutions.
4. Enhanced data availability, reliability, and platform resilience.
5. Improved integration across cloud applications and enterprise systems.
6. Strengthened cybersecurity and enterprise data governance.
7. Increased productivity through automation and DataOps practices.
8. Improved real-time analytics and data-driven decision-making.
9. Reduced infrastructure maintenance and deployment complexity.
10. Enhanced organizational readiness for artificial intelligence and digital transformation.

Target Audiences
 

1. Cloud architects and cloud infrastructure engineers.
2. Data engineers and senior data engineering professionals.
3. Data platform architects and enterprise architects.
4. DevOps engineers and Site Reliability Engineers.
5. Big data engineers and analytics professionals.
6. Database administrators and data warehouse specialists.
7. Software developers and cloud application engineers.
8. IT managers, technology consultants, and digital transformation leaders.

Course Duration: 5 days

Course Modules

Module 1: Cloud-Native Data Platform Architecture
 

• Principles of cloud-native architecture and distributed data systems.
• Enterprise data platform design and architectural patterns.
• AWS, Microsoft Azure, and Google Cloud Platform ecosystems.
• Data lakes, lakehouses, and cloud data warehouse integration.
• Scalability, elasticity, availability, and fault-tolerant architecture.
• Case study: Netflix's cloud-native architecture for large-scale data processing.

Module 2: Containerization and Kubernetes for Data Engineering
 

• Docker containerization and image management.
• Kubernetes architecture, clusters, and workload orchestration.
• Deploying distributed data processing applications.
• Managing persistent storage and container networking.
• Kubernetes security, scaling, and workload optimization.
• Case study: Spotify's use of containerized infrastructure for scalable engineering workloads.

Module 3: Cloud Data Lakes and Lakehouse Engineering
 

• Cloud object storage and distributed data management.
• Data lake architecture using Amazon S3 and Azure Data Lake Storage.
• Lakehouse engineering with Delta Lake and Apache Iceberg.
• Data partitioning, metadata management, and schema evolution.
• Data ingestion, transformation, and storage optimization.
• Case study: Databricks Lakehouse implementations for enterprise analytics.

Module 4: Distributed Data Processing and Streaming
 

• Apache Spark architecture and distributed computing.
• Apache Kafka event streaming and message processing.
• Real-time data ingestion and stream processing.
• Batch processing and parallel computing strategies.
• Data pipeline performance optimization and fault tolerance.
• Case study: Uber's real-time data processing and streaming architecture.

Module 5: DataOps, DevOps, and CI/CD Automation
 

• DataOps principles and modern data engineering workflows.
• Continuous integration and continuous delivery for data pipelines.
• Git-based version control and collaborative development.
• Automated testing, deployment, and pipeline validation.
• Workflow orchestration using Apache Airflow.
• Case study: Airbnb's data infrastructure automation and engineering practices.

Module 6: Infrastructure as Code and Cloud Automation
 

• Infrastructure as code principles and implementation.
• Terraform configuration and automated provisioning.
• Cloud resource management and environment standardization.
• Serverless computing and cloud-native automation.
• Configuration management and deployment automation.
• Case study: Capital One's cloud infrastructure modernization and automation.

Module 7: Data Security, Governance, and Observability
 

• Cloud-native identity and access management.
• Data encryption, privacy, and regulatory compliance.
• Metadata management and enterprise data governance.
• Platform monitoring, logging, and distributed tracing.
• Reliability engineering, incident management, and recovery.
• Case study: Google Cloud's enterprise security and data governance practices.

Module 8: Performance Engineering and Enterprise Capstone
 

• Cloud-native data platform performance benchmarking.
• Resource allocation, autoscaling, and cost optimization.
• High availability, disaster recovery, and business continuity.
• Integration of artificial intelligence and machine learning workloads.
• Enterprise cloud-native platform implementation project.
• Case study: LinkedIn's distributed data infrastructure and large-scale analytics ecosystem.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical cloud-native architecture design exercises.
• Hands-on Docker, Kubernetes, Terraform, and Apache Spark demonstrations.
• Group assignments involving distributed data engineering challenges.
• International enterprise case studies and technology assessments.
• Capstone projects, knowledge assessments, and implementation workshops.

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.