Data Analytics, Artificial Intelligence and Decision Sciences
Integrating Cloud Computing with Data Analytics
Please select a city/session before registration.
About this program
Cloud computing and data analytics have emerged as fundamental components of digital transformation. Cloud platforms offer the scalability and adaptability essential for handling vast amounts of data, while analytics convert this data into insightful information that supports strategic decisions.
This course offers hands-on guidance on merging cloud services with analytics platforms. Participants will explore cloud-based data pipelines, storage solutions, visualization techniques, and machine learning applications. The emphasis is on generating business value through the seamless fusion of cloud infrastructure and sophisticated analytics.
At EuroQuest International Training, the curriculum blends technical expertise with business strategy, equipping participants to design cloud-enabled analytics frameworks that foster innovation and drive growth.
Key outcomes
- Comprehend cloud computing architectures and service models
- Develop and oversee cloud-based data pipelines
- Utilize analytics tools for business intelligence within cloud environments
- Implement machine learning and AI on cloud platforms
- Maintain data governance, regulatory compliance, and security in cloud systems
- Integrate both structured and unstructured data sources
- Align cloud data strategies with organizational objectives
- Optimize expenditure and resource allocation in cloud deployments
- Implement real-time analytics solutions
- Effectively communicate insights using visualization and dashboard tools
- Manage analytics environments across hybrid and multi-cloud setups
- Formulate an action plan to advance cloud–analytics integration maturity
Who should attend
- Cloud architects and engineers
- Data analysts and data scientists
- IT and digital transformation managers
- Business intelligence specialists
- Risk, compliance, and governance professionals
Course outline
Unit 1: Overview of Cloud Technology and Data Analytics
- Historical development of cloud and analytics convergence
- Key business motivations and advantages
- Major trends shaping the industry
- Illustrative case studies of analytics powered by cloud
Unit 2: Architectures in Cloud Computing
- Utilization of SaaS, PaaS, and IaaS for analytics
- Comparing cloud-native with hybrid architectural models
- Considerations for public, private, and multi-cloud environments
- Strategies for scalability and cost efficiency
Unit 3: Cloud-Based Data Storage and Management
- Differences between data lakes and data warehouses
- Cloud-native solutions for data storage
- Handling structured versus unstructured datasets
- Maintaining data availability and dependability
Unit 4: Developing Data Pipelines within Cloud Environments
- ETL and ELT methodologies on cloud platforms
- Combining data from diverse sources
- Comparison of stream processing and batch processing
- Automation tools and platforms for pipeline creation
Unit 5: Integrating Analytics Tools with Cloud Platforms
- Cloud-based business intelligence applications
- Connecting widely-used analytics platforms
- Techniques for visualization and dashboard creation
- Practical sessions using cloud BI tools
Unit 6: Cloud-Based Machine Learning
- AI and ML offerings from major cloud providers
- Procedures for model training and deployment
- Predictive analytics use cases
- Automating machine learning workflows
Unit 7: Big Data and Real-Time Analytics
- Handling streaming data processing
- Integration of IoT data within the cloud
- Creation of real-time dashboards and alert systems
- Big data use cases implemented in cloud environments
Unit 8: Security, Compliance, and Governance in Cloud
- Frameworks for data governance in cloud settings
- Compliance with regulations such as GDPR and HIPAA
- Implementing security measures and encryption
- Managing risks within cloud data strategies
Unit 9: Cloud Expense Management and Return on Investment
- Controlling cloud resource usage
- Understanding pricing structures and cost management
- Aligning cloud spending with business objectives
- Evaluating ROI from cloud analytics projects
Unit 10: Analytics in Hybrid and Multi-Cloud Environments
- Design principles for analytics across multiple clouds
- Challenges of integration and interoperability
- Approaches to avoiding vendor lock-in
- Case studies highlighting multi-cloud analytics deployments
Unit 11: Effectively Communicating Analytics Findings
- Approaches to data storytelling
- Creating dashboards for executive audiences
- Converting technical analytics into business insights
- Collaboration and reporting tools
Unit 12: Final Project on Cloud and Analytics Integration
- Creating a framework for cloud-enabled analytics
- Collaborative integration project
- Delivering presentations of insights and strategic recommendations
- Developing an action plan for organizational implementation