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Course Overview

Cloud computing and data analytics have become inseparable pillars of digital transformation. Cloud platforms provide the scalability and flexibility needed to process massive volumes of data, while analytics turn that data into actionable insights for strategic decision-making.

This course provides practical guidance on integrating cloud services with analytics platforms. Participants will learn about cloud-based data pipelines, storage, visualization, and machine learning applications. The focus is on delivering business value through seamless integration of cloud infrastructure and advanced analytics.

At EuroQuest International Training, the program combines technical knowledge with business strategy, ensuring participants can design cloud-enabled analytics frameworks that drive innovation and growth.

Key Benefits of Attending

• Learn to design cloud-based data pipelines and architectures

• Apply analytics and machine learning in cloud environments

• Strengthen governance, security, and compliance in cloud data strategies

• Improve efficiency and scalability of business analytics

• Enhance decision-making through integrated data insights

Why Attend

This course empowers professionals to maximize the value of cloud adoption by integrating advanced analytics, ensuring organizations are data-driven, agile, and innovation-focused.

Course Methodology

• Expert-led cloud and analytics sessions

• Hands-on labs with cloud platforms and data tools

• Case studies of cloud analytics adoption

• Group exercises on architecture design

• Simulation of cloud-enabled business analytics projects

Course Objectives

By the end of this ten-day training course, participants will be able to:

• Understand cloud computing architectures and service models

• Design and manage cloud-based data pipelines

• Apply analytics tools for business intelligence in the cloud

• Use machine learning and AI in cloud platforms

• Ensure data governance, compliance, and security in cloud systems

• Integrate structured and unstructured data sources

• Align cloud data strategies with organizational goals

• Optimize costs and resources in cloud deployments

• Deploy real-time analytics solutions

• Communicate insights through visualization and dashboards

• Manage hybrid and multi-cloud analytics environments

• Build an action plan for cloud–analytics integration maturity

Target Audience

• Cloud architects and engineers

• Data analysts and data scientists

• IT and digital transformation managers

• Business intelligence professionals

• Risk, compliance, and governance leaders

Target Competencies

• Cloud architecture and deployment

• Data pipeline design and management

• Analytics and machine learning integration

• Data governance and compliance

• Real-time business intelligence

• Hybrid and multi-cloud strategy

• Strategic decision-making with data

Course Outline

Unit 1: Introduction to Cloud and Data Analytics

• Evolution of cloud and analytics integration

• Business drivers and benefits

• Key industry trends

• Case studies of cloud-enabled analytics

Unit 2: Cloud Computing Architectures

• SaaS, PaaS, and IaaS in analytics

• Cloud-native vs hybrid architectures

• Public, private, and multi-cloud considerations

• Scalability and cost optimization

Unit 3: Data Storage and Management in the Cloud

• Data lakes vs warehouses

• Cloud-native storage solutions

• Structured vs unstructured data handling

• Ensuring data reliability and availability

Unit 4: Building Data Pipelines in the Cloud

• ETL/ELT processes in cloud platforms

• Integrating multiple data sources

• Stream vs batch processing

• Tools and platforms for pipeline automation

Unit 5: Analytics Tools and Cloud Integration

• Business intelligence in the cloud

• Integrating popular analytics platforms

• Visualization and dashboarding

• Hands-on lab with cloud BI tools

Unit 6: Machine Learning in the Cloud

• AI/ML services in leading cloud platforms

• Model training and deployment workflows

• Use cases for predictive analytics

• Automation of ML pipelines

Unit 7: Real-Time and Big Data Analytics

• Processing streaming data

• IoT data integration in the cloud

• Real-time dashboards and alerts

• Case studies of big data in the cloud

Unit 8: Governance, Security, and Compliance

• Data governance frameworks for the cloud

• Regulatory requirements (GDPR, HIPAA, etc.)

• Security controls and encryption practices

• Risk management in cloud data strategies

Unit 9: Cloud Cost Optimization and ROI

• Managing cloud resource consumption

• Pricing models and cost controls

• Aligning cloud investments with business value

• Measuring ROI of cloud analytics initiatives

Unit 10: Hybrid and Multi-Cloud Analytics

• Designing multi-cloud analytics architectures

• Integration and interoperability challenges

• Vendor lock-in mitigation strategies

• Case studies of multi-cloud analytics

Unit 11: Communicating Analytics Insights

• Data storytelling techniques

• Designing executive dashboards

• Translating technical insights into business value

• Tools for collaboration and reporting

Unit 12: Capstone Cloud & Analytics Integration Project

• Designing a cloud-enabled analytics framework

• Group-based integration exercise

• Presentation of insights and recommendations

• Action plan for organizational adoption

Closing Call to Action

Join this ten-day training course to master cloud computing and data analytics integration, equipping your organization to unlock insights, scalability, and digital innovation.