Designing Edge to AI Applications on Hybrid Data Cloud Course
- Duration: 32 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Cloudera Data Platform: Designing Edge to AI Applications on Hybrid Data Cloud course is designed to empower data architects, solution designers, and enterprise technologists with the skills to architect and build end-to-end data applications that span edge environments, centralized analytics, and AI workflows on the Hybrid Data Cloud (HDC). Participants will learn how to integrate edge data ingestion, real-time processing, governance, analytics, and machine learning capabilities using CDP services. The training emphasizes architectural best practices, scalability, security, and real-world enterprise design patterns that support data-driven decision-making from edge to AI.
Course Details
- Deep mastery of hybrid cloud and edge-to-AI architectural patterns
- Ability to design scalable data pipelines and AI-enabled workflows
- Enhanced expertise in integrating edge sources with core analytics platforms
- Knowledge of governance, security, and compliance in distributed systems
- Hands-on insights into Cloudera HDC services for analytics and machine learning
- Streamlined readiness for advanced architectural and leadership roles
- Open to cloud architects, data architects, enterprise solution designers, and technical leaders
- Ideal for professionals responsible for strategic data platform design
- Prior experience with cloud environments, distributed systems, and data analytics is recommended
- Familiarity with big data platforms, AI/ML workloads, and edge technologies is beneficial
- Corporate sponsorship or group participation is encouraged
- Master foundational and advanced design principles for hybrid data cloud applications
- Understand how to ingest and process edge data for analytics and AI workloads
- Gain expertise in designing end-to-end data applications with security, governance, and scalability
- Learn integration patterns for edge sources, centralized data lakes, and AI platforms
- Develop capabilities in deploying AI models and analytics pipelines in hybrid environments
- Achieve readiness to lead architectural initiatives that span edge to AI
- Introduction to Hybrid Data Cloud & Edge Analytics
- Architectural Components of CDP and HDC
- Edge Data Ingestion & Preprocessing Patterns
- Real-Time Data Processing & Streaming Workflows
- Data Governance & Security for Hybrid Architectures
- Analytics Pipelines & Scalable Data Workloads
- Machine Learning & AI Integration on CDP
- Hybrid Deployment Patterns & Best Practices
- Operational Considerations in Hybrid Environments
- Hands-On Labs and Enterprise Use Cases
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