Course Introduction
The AI-Native Data Lakehouse Architecture Training Course provides a comprehensive understanding of how modern lakehouse platforms can be designed specifically to support artificial intelligence, machine learning, generative AI, advanced analytics, and intelligent data applications. Participants explore AI-ready architecture, scalable storage, processing, metadata, vector data, semantic capabilities, governance, and intelligent automation across enterprise data environments.
The course examines how AI-native lakehouse architectures extend conventional data platform capabilities by integrating data engineering, analytical processing, machine learning, model development, real-time data, and AI application workloads within a unified environment. Participants learn how architecture decisions around storage, compute, metadata, data quality, lineage, security, and orchestration influence the reliability and performance of AI-driven solutions.
Participants will explore the data foundations required for AI workloads, including structured, semi-structured, unstructured, streaming, multimodal, and vectorized data. The programme addresses ingestion, transformation, feature engineering, embeddings, vector search, retrieval-augmented generation, semantic layers, model context, data preparation, and AI application integration while emphasizing the importance of trustworthy and well-governed data.
The programme also focuses on governance and operational requirements for AI-native lakehouses. Participants examine data quality, metadata, lineage, privacy, access controls, model governance, AI security, responsible AI practices, observability, cost management, and lifecycle controls. These capabilities are considered across cloud, hybrid, multi-cloud, and distributed architectures where data and AI workloads must operate securely and reliably.
Emerging technologies are integrated throughout the course, including generative AI, retrieval-augmented generation, vector databases and vectorized lakehouse architectures, AI agents, multimodal analytics, semantic knowledge layers, intelligent metadata, automated data engineering, and machine-assisted governance. Participants evaluate how these capabilities can enhance AI development while addressing accuracy, data exposure, model context, explainability, hallucination risks, and operational oversight.
By the end of the programme, participants will be equipped to design, evaluate, implement, and optimize AI-native data lakehouse architectures. They will gain practical frameworks for building scalable AI-ready data foundations, integrating advanced analytics and machine learning workloads, supporting generative AI applications, strengthening governance, and aligning lakehouse architecture with enterprise AI strategies and measurable business outcomes.
Duration
5 days
Who Should Attend
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Data architects designing AI-ready lakehouse platforms and modern enterprise data architectures.
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Enterprise architects developing strategies for artificial intelligence, cloud data modernization, analytics, and distributed data environments.
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AI architects responsible for designing scalable data foundations for generative AI, machine learning, AI agents, and intelligent applications.
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Data engineers building ingestion pipelines, transformation workflows, feature engineering processes, and AI-ready data platforms.
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Machine learning engineers developing models, features, embeddings, vector search capabilities, and production AI applications.
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Data platform engineers responsible for storage, compute, metadata, orchestration, security, observability, and platform operations.
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Data governance professionals addressing metadata, lineage, quality, privacy, security, compliance, and responsible AI requirements.
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Chief Data Officers and AI leaders developing enterprise data and artificial intelligence modernization strategies.
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Analytics and business intelligence professionals working with advanced analytical workloads and AI-enabled data platforms.
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Cloud architects evaluating scalable infrastructure for AI, machine learning, real-time analytics, and modern lakehouse environments.
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Technology managers assessing AI-native lakehouse platforms, architectures, tools, integration patterns, and operational capabilities.
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Digital transformation leaders seeking to establish modern data foundations for enterprise AI adoption and intelligent business applications.
Course Objectives
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Develop a comprehensive understanding of AI-native data lakehouse principles, architecture patterns, components, capabilities, use cases, and enterprise implementation requirements.
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Explain how lakehouse storage, compute, metadata, processing, vector capabilities, semantic layers, governance, and AI services combine to support intelligent workloads.
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Design scalable data architectures capable of supporting structured, unstructured, multimodal, streaming, vectorized, analytical, machine learning, and generative AI workloads.
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Apply data engineering and integration patterns that prepare enterprise data for model development, retrieval-augmented generation, AI agents, advanced analytics, and intelligent applications.
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Design effective approaches for embeddings, vector search, feature engineering, semantic enrichment, retrieval pipelines, and contextual data preparation within lakehouse environments.
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Establish governance, security, privacy, lineage, quality, access management, and responsible AI controls that protect data and improve trust in AI-enabled lakehouse workloads.
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Optimize AI-native lakehouse performance through appropriate storage layouts, compute selection, workload management, caching, partitioning, vector indexing, and resource allocation.
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Integrate observability and operational practices for monitoring pipelines, data quality, AI workloads, model dependencies, vector systems, performance, costs, and platform reliability.
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Evaluate emerging AI-native capabilities including generative AI, AI agents, multimodal data processing, intelligent metadata, semantic layers, and automated data engineering.
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Develop an actionable AI-native lakehouse implementation roadmap that aligns architecture, data strategy, AI initiatives, governance, technology investment, skills, and measurable business outcomes.
Comprehensive Course Outline
Module 1: Foundations of AI-Native Data Lakehouse Architecture
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Understanding AI-native lakehouse concepts, principles, architecture patterns, use cases, capabilities, and strategic importance.
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Examining how AI workloads change traditional data platform requirements for storage, processing, metadata, governance, performance, and accessibility.
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Comparing conventional data lakes, warehouses, lakehouses, AI data platforms, and AI-native lakehouse architectures across enterprise use cases.
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Identifying technical and organizational requirements for creating scalable data foundations that support rapidly evolving AI applications.
Module 2: AI-Ready Lakehouse Architecture and Platform Design
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Designing logical and physical architectures covering storage, compute, metadata, processing, orchestration, AI services, vector capabilities, and governance.
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Establishing architectural principles for scalability, interoperability, reliability, security, observability, automation, and AI workload flexibility.
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Designing cloud, hybrid, multi-cloud, and distributed lakehouse environments that support different AI and analytical workload requirements.
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Aligning AI-native lakehouse architecture with enterprise architecture standards, AI strategies, data modernization objectives, and technology roadmaps.
Module 3: AI-Ready Data Engineering and Ingestion
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Designing batch, streaming, micro-batch, API, event-driven, and change-data-capture pipelines for AI-ready enterprise data environments.
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Integrating structured, semi-structured, unstructured, multimodal, transactional, analytical, and external data sources into lakehouse platforms.
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Managing schema evolution, data drift, late-arriving data, duplicate records, failed ingestion, pipeline dependencies, and recovery processes.
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Applying automated testing, orchestration, validation, monitoring, and deployment practices to improve the reliability of AI data pipelines.
Module 4: Data Preparation, Feature Engineering, and Semantic Context
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Designing transformation processes that prepare high-quality data for machine learning, generative AI, analytical models, and intelligent applications.
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Applying feature engineering, data enrichment, normalization, chunking, labeling, embedding generation, and contextualization techniques for AI workloads.
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Developing semantic layers and contextual metadata that improve the ability of AI systems to interpret and use enterprise information appropriately.
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Managing reusable data preparation workflows, feature assets, dependencies, versioning, testing, and deployment across AI development environments.
Module 5: Vector Data, Embeddings, and Retrieval-Augmented Generation
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Understanding embeddings, vector representations, similarity search, vector indexing, and their role in modern AI-native data architectures.
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Designing lakehouse architectures that support vectorized data alongside structured and unstructured enterprise information.
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Building retrieval-augmented generation patterns that connect enterprise data, retrieval mechanisms, language models, and AI applications.
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Managing chunking, indexing, metadata, retrieval relevance, freshness, security, and context quality within enterprise retrieval pipelines.
Module 6: Generative AI, AI Agents, and Intelligent Data Applications
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Designing data foundations that support generative AI applications, copilots, conversational systems, intelligent search, and enterprise AI assistants.
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Exploring AI agent architectures that interact with enterprise data, tools, workflows, APIs, metadata, and analytical services.
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Integrating lakehouse data with model-serving environments, prompt workflows, retrieval systems, tools, and intelligent application layers.
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Establishing controls for AI-generated outputs, data access, context validation, hallucination management, auditability, and human oversight.
Module 7: Metadata, Governance, Security, and Responsible AI
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Establishing metadata and catalogue capabilities that support AI data discovery, lineage, classification, ownership, context, and responsible data usage.
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Implementing data quality, provenance, lineage, access control, privacy, encryption, and security measures across AI-native lakehouse environments.
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Designing governance controls for generative AI, machine learning, automated decision support, vector data, embeddings, and AI agents.
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Addressing responsible AI requirements involving transparency, explainability, bias management, data protection, human oversight, and regulatory compliance.
Module 8: AI-Native Lakehouse Performance, Scalability, and Cost
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Optimizing lakehouse storage, compute, partitioning, clustering, caching, indexing, and workload management for AI and analytical workloads.
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Designing scalable architectures for concurrent model training, inference, retrieval, vector search, streaming analytics, and business intelligence workloads.
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Managing resource allocation across data engineering, machine learning, generative AI, analytical, and real-time processing workloads.
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Establishing FinOps and operational cost controls covering storage, compute, vector processing, model workloads, data movement, and AI service consumption.
Module 9: Observability, Reliability, and AI Data Operations
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Designing observability frameworks for pipelines, datasets, metadata, vector indexes, retrieval systems, AI workloads, and platform infrastructure.
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Monitoring data freshness, quality, drift, lineage, pipeline health, model dependencies, retrieval performance, latency, and service availability.
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Establishing automated alerting, incident management, root cause analysis, remediation, and recovery processes for critical AI data services.
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Developing operational practices that connect data reliability, AI application performance, governance requirements, and continuous platform improvement.
Module 10: AI-Native Lakehouse Implementation and Future Trends
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Developing phased implementation roadmaps covering architecture, priority AI use cases, data foundations, platform capabilities, governance, and organizational readiness.
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Defining measurable success indicators for data quality, AI workload performance, retrieval effectiveness, platform adoption, reliability, cost, and business value.
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Exploring emerging developments including agentic AI, multimodal lakehouses, semantic data platforms, autonomous data engineering, intelligent governance, and AI-native data products.
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Creating an enterprise action plan that connects AI-native lakehouse modernization with artificial intelligence strategy, analytics, governance, digital transformation, and business objectives.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
