Research and Data Analysis

Cloud Computing for Large-Scale Data Analysis (AWS, Azure, GCP) Training Course

Introduction

Cloud computing has revolutionized how organizations manage, store, and analyze large-scale data. As the demand for scalable, secure, and efficient data processing grows, the integration of platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) becomes essential. This course empowers participants to harness cloud infrastructure to perform complex data analysis, implement distributed computing techniques, and optimize big data workflows with modern cloud-native tools.

Cloud Computing for Large-Scale Data Analysis Training Course is designed to deliver hands-on experience in deploying cloud-based data solutions using leading platforms. Whether it's real-time data ingestion, machine learning integration, or automated pipeline development, learners will gain critical skills needed to drive innovation and digital transformation in data-driven industries.

Programme Curriculum

Cloud Computing for Large-Scale Data Analysis Training Course

Introduction

Course Objectives

Understand the fundamentals of cloud computing in the context of big data analytics
Explore key differences between AWS, Azure, and GCP for data workloads
Deploy scalable storage and compute resources using cloud-native tools
Implement distributed data processing with Hadoop and Spark on the cloud
Optimize data pipelines for performance and cost-efficiency
Utilize cloud-based databases (Redshift, BigQuery, Azure Synapse) for analytics
Configure and manage cloud-based ETL workflows
Analyze real-time and batch data using managed services
Integrate AI and ML models into cloud analytics pipelines
Ensure data security, governance, and compliance on cloud platforms
Use Kubernetes and Docker for cloud-based data containerization
Monitor and troubleshoot large-scale analytics systems in the cloud
Plan and architect end-to-end cloud solutions for enterprise-level data projects

Target Audience

Data Scientists
Cloud Engineers
IT Managers
Business Intelligence Analysts
DevOps Professionals
System Architects
AI/ML Engineers
Graduate Students in Data Analytics

Course Duration: 5 days

Course Modules

Module 1: Introduction to Cloud Computing for Data Analysis

Overview of cloud ecosystems (AWS, Azure, GCP)
Benefits and limitations of cloud computing for big data
Key service models (IaaS, PaaS, SaaS)
Storage and compute services overview
Role of virtualization and containers
Case Study: Migrating an on-premise data warehouse to AWS

Module 2: Cloud Storage and Compute Essentials

Cloud storage types: Object, Blob, File
Compute options: EC2, Azure VMs, GCP Compute Engine
Resource provisioning and scaling
Security and IAM configuration
Cost management and budgeting
Case Study: Choosing the right compute/storage strategy for e-commerce data

Module 3: Distributed Data Processing with Hadoop and Spark

Installing and configuring Hadoop/Spark on cloud platforms
Spark vs. Hadoop: Use cases and performance
Running MapReduce jobs on the cloud
Integrating with S3, Blob Storage, or GCS
Cluster management with EMR, Dataproc, and HDInsight
Case Study: Real-time sentiment analysis using Spark on GCP Dataproc

Module 4: Cloud-Based ETL and Data Pipelines

Building ETL workflows with AWS Glue, Azure Data Factory, GCP Dataflow
Data ingestion tools: Kinesis, Event Hubs, Pub/Sub
Automating data transformation
Handling unstructured data
Orchestrating workflows with Apache Airflow and cloud-native tools
Case Study: Streaming IoT sensor data into BigQuery using GCP pipelines

Module 5: Cloud Databases and Data Warehousing

Introduction to cloud-native databases (RDS, Cosmos DB, Cloud SQL)
Data warehousing: Redshift, BigQuery, Synapse Analytics
Query optimization techniques
Connecting BI tools (Tableau, Power BI) to cloud databases
Backup, replication, and disaster recovery
Case Study: Implementing a centralized cloud data warehouse for a logistics firm

Module 6: Integrating Machine Learning in Cloud Analytics

ML model deployment using SageMaker, Azure ML, Vertex AI
Training models on cloud GPUs and TPUs
Integrating ML into ETL pipelines
Monitoring and retraining models
Ethical AI and compliance in cloud platforms
Case Study: Predicting customer churn using Azure ML integrated with Data Factory

Module 7: Securing Data in the Cloud

Identity and Access Management (IAM)
Encryption at rest and in transit
Compliance standards (GDPR, HIPAA, SOC2)
Monitoring access and audit logs
Risk assessment and threat detection tools
Case Study: Implementing a secure data lake architecture for a healthcare provider

Module 8: Cloud Architecture, Monitoring, and Optimization

Designing cloud-native data solutions
High availability and fault tolerance
Cost analysis and billing optimization
Logging and monitoring with CloudWatch, Azure Monitor, Stackdriver
Performance tuning for large-scale analytics
Case Study: Building a cost-efficient multi-cloud architecture for real-time analytics

Training Methodology

Instructor-led virtual training with cloud lab environments
Hands-on exercises using AWS, Azure, and GCP consoles
Real-world case studies and industry scenarios
Capstone project and group presentations
Weekly assessments and practical quizzes

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Cloud platforms scalable computation research analytics

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000