AI-Native Lakehouse Engineering for Agentic Analytics Training Course

Course Introduction

AI-Native Lakehouse Engineering for Agentic Analytics represents an advanced evolution of modern data platforms, combining lakehouse architecture, intelligent data engineering, generative AI, autonomous agents, semantic data services, and analytical automation. This training course examines how enterprises can engineer lakehouse environments specifically designed to support agentic analytical workflows in which AI agents can discover data, interpret business context, construct analytical queries, evaluate evidence, generate insights, and initiate governed actions under defined organizational policies.

The course provides an in-depth exploration of AI-native lakehouse architecture, focusing on how storage, compute, metadata, semantic layers, governance, observability, vector capabilities, knowledge graphs, and analytical services can be engineered to support intelligent agents. Participants will learn how to create machine-accessible data environments where AI systems can discover trusted information, understand relationships and business definitions, reason across multiple datasets, and produce traceable analytical outputs. The program emphasizes architectural patterns that connect conventional data engineering with emerging agentic computing models.

A major focus is placed on engineering reliable data foundations for agentic analytics. Participants will examine data ingestion, transformation, quality engineering, metadata enrichment, lineage, semantic modeling, data contracts, feature engineering, retrieval systems, and real-time processing as interconnected capabilities. Particular attention is given to ensuring that AI agents operate on current, governed, high-quality, and contextually meaningful information. The course demonstrates how data quality, observability, provenance, and semantic consistency become essential engineering requirements when analytical decisions are increasingly delegated to intelligent systems.

The program also explores agentic analytical architectures in which AI agents interact with lakehouse services, SQL engines, semantic layers, catalogues, APIs, notebooks, dashboards, knowledge graphs, and workflow systems. Participants will investigate agent orchestration, tool use, retrieval-augmented generation, query planning, analytical reasoning, multi-agent patterns, context management, memory, evaluation, and controlled execution. Governance mechanisms are integrated throughout these architectures to address authorization, privacy, policy enforcement, auditability, provenance, human oversight, and protection against inappropriate or unreliable automated actions.

Participants will further examine how AI-native lakehouse platforms can support real-time decision intelligence, autonomous data operations, intelligent business monitoring, predictive analytics, and enterprise knowledge systems. Advanced topics include agent-ready data products, semantic contracts, vector-enabled lakehouse architectures, knowledge graphs, AI-assisted data engineering, automated data quality remediation, intelligent workload optimization, and machine-verifiable governance. The course connects these capabilities to practical enterprise scenarios where agents continuously monitor information, investigate anomalies, answer analytical questions, generate explanations, and coordinate governed workflows.

By the end of the training, participants will be equipped to design and engineer AI-native lakehouse environments capable of supporting reliable agentic analytics at enterprise scale. The course provides practical frameworks for architecture design, data engineering, semantic enablement, agent integration, security, governance, observability, evaluation, cost management, and implementation planning. Participants will gain the knowledge required to transform conventional lakehouse platforms into intelligent analytical foundations that can support increasingly autonomous data discovery, reasoning, insight generation, and decision-support workflows.

Duration

10 days

Who Should Attend

• Enterprise Data Architects designing AI-native lakehouse and intelligent analytical architectures.

• Lakehouse Architects responsible for modernizing enterprise data platforms for AI and agentic workloads.

• Data Engineers building scalable ingestion, transformation, processing, quality, and metadata pipelines.

• AI Engineers developing agentic analytics systems and intelligent data interaction capabilities.

• Machine Learning Engineers integrating AI models, feature platforms, retrieval systems, and lakehouse environments.

• Generative AI Engineers designing retrieval-augmented and tool-using analytical applications.

• Analytics Engineers developing semantic models, analytical transformations, metrics, and consumption-ready data assets.

• Data Platform Engineers building self-service infrastructure and shared capabilities for AI-enabled data workloads.

• Data Scientists developing advanced analytical solutions that combine lakehouse data with AI reasoning systems.

• MLOps and LLMOps Professionals managing model, agent, evaluation, deployment, and operational lifecycles.

• Data Governance Professionals responsible for policies, lineage, privacy, security, quality, and AI governance.

• Data Quality and Observability Professionals establishing trust and reliability for AI-consumed enterprise data.

• Enterprise Architects integrating AI-native data capabilities into broader technology and digital transformation strategies.

• Business Intelligence and Analytics Leaders implementing intelligent and automated decision-support capabilities.

• Technology Managers and Data Transformation Leaders overseeing enterprise lakehouse and AI modernization initiatives.

Course Objectives

• Explain the architectural principles of AI-native lakehouses and their role in enabling agentic analytics, intelligent data services, and autonomous analytical workflows.

• Design lakehouse architectures that provide AI agents with scalable access to governed, discoverable, high-quality, contextualized, and machine-consumable enterprise data.

• Engineer ingestion and transformation pipelines that deliver reliable batch, streaming, incremental, and real-time data foundations for continuously operating analytical agents.

• Implement metadata, lineage, semantic models, catalogues, knowledge graphs, and contextual information services that allow agents to discover and correctly interpret enterprise data.

• Apply data quality engineering and observability techniques that continuously assess freshness, completeness, accuracy, consistency, validity, and reliability of agent-consumed information.

• Design agentic analytics architectures involving AI agents, analytical engines, semantic layers, retrieval systems, APIs, tools, workflow platforms, and governed lakehouse services.

• Implement retrieval-augmented generation and tool-use patterns that allow analytical agents to retrieve evidence, execute controlled queries, reason over results, and produce traceable outputs.

• Establish security and governance controls covering identity, authorization, privacy, data access, policy enforcement, provenance, auditability, human oversight, and agent action boundaries.

• Design semantic and analytical data products that provide consistent business definitions, reusable metrics, relationships, context, and trusted information for intelligent agents.

• Integrate real-time streaming, event-driven processing, anomaly detection, predictive analytics, and continuous monitoring into agentic analytical environments.

• Apply evaluation, monitoring, observability, and reliability engineering practices to measure agent accuracy, analytical correctness, grounding, latency, cost, and operational behavior.

• Develop enterprise implementation strategies for AI-native lakehouse adoption covering architecture modernization, platform engineering, governance, operating models, risk management, and continuous optimization.

Comprehensive Course Outline

Module 1: Foundations of AI-Native Lakehouse Engineering

• Evolution of data lakes and lakehouses toward AI-native architectures designed for intelligent analytics and autonomous data interaction.

• Core concepts connecting lakehouse engineering, generative AI, analytical agents, semantic systems, retrieval, and intelligent automation.

• Architectural characteristics of AI-native data platforms including machine accessibility, contextual richness, observability, governance, and interoperability.

• Enterprise use cases for agentic analytics across business intelligence, operations, monitoring, research, decision support, and intelligent automation.

Module 2: AI-Native Lakehouse Reference Architecture

• Reference architectures integrating storage, compute, metadata, semantic layers, vector services, analytical engines, agents, governance, and orchestration.

• Designing interfaces between lakehouse services and AI agents for controlled data discovery, querying, reasoning, and analytical execution.

• Architectural patterns for separating data storage, analytical computation, agent reasoning, tool execution, and workflow orchestration.

• Resilience, scalability, availability, interoperability, and architectural lifecycle considerations for enterprise AI-native lakehouse platforms.

Module 3: AI-Ready Data Storage and Open Table Architecture

• Engineering scalable storage architectures using open data formats, transactional tables, metadata layers, and cloud-native object storage.

• Designing tables and storage structures optimized for analytical agents requiring efficient filtering, aggregation, retrieval, historical analysis, and contextual access.

• Time travel, versioning, transactional consistency, schema evolution, data retention, and reproducibility for AI-driven analytical workloads.

• File optimization, partitioning, compaction, clustering, indexing, caching, and storage lifecycle strategies for intelligent query workloads.

Module 4: Intelligent Data Ingestion and Transformation

• Designing batch, streaming, incremental, API, event-driven, and change-data-capture ingestion pipelines for continuously available AI-ready information.

• Automated schema discovery, validation, transformation, enrichment, standardization, and contextualization across heterogeneous enterprise sources.

• Engineering transformation pipelines that preserve semantic meaning, lineage, provenance, and business context required by analytical agents.

• AI-assisted data engineering for pipeline development, transformation recommendations, anomaly detection, documentation, and operational troubleshooting.

Module 5: Semantic Data Engineering for Agentic Analytics

• Designing semantic layers that expose business concepts, entities, metrics, relationships, dimensions, and analytical definitions to intelligent agents.

• Semantic modeling techniques for improving agent understanding, query accuracy, business-context interpretation, and analytical consistency.

• Metric stores, business glossaries, ontology structures, semantic contracts, and governed definitions for enterprise analytical systems.

• Connecting semantic models to lakehouse tables, catalogues, APIs, knowledge graphs, dashboards, and agent tool interfaces.

Module 6: Metadata, Knowledge Graphs and Context Engineering

• Designing metadata architectures that provide agents with information about data meaning, ownership, quality, lineage, sensitivity, relationships, and usage.

• Knowledge graph architectures representing entities, relationships, business concepts, processes, systems, and contextual enterprise knowledge.

• Context engineering techniques for supplying analytical agents with relevant, current, trusted, and task-specific information.

• Active metadata patterns that support intelligent discovery, automated governance, agent planning, dependency analysis, and analytical workflows.

Module 7: Data Quality, Observability and AI Trust

• Engineering data quality controls for information consumed by AI agents, including accuracy, completeness, freshness, validity, consistency, and uniqueness.

• Data observability architectures for detecting pipeline failures, schema changes, anomalies, unexpected distributions, and degraded data reliability.

• AI trust mechanisms involving provenance, evidence tracking, source attribution, confidence indicators, validation, and reproducibility.

• Automated remediation, incident management, quality gates, and reliability engineering for continuously operating agentic analytical systems.

Module 8: Agentic Analytics Architecture and Design

• Core architecture of analytical agents including planning, reasoning, tool selection, execution, observation, memory, and response generation.

• Designing agent interfaces with SQL engines, semantic layers, data catalogues, APIs, notebooks, dashboards, and analytical services.

• Single-agent and multi-agent architectures for data discovery, investigative analytics, reporting, forecasting, monitoring, and decision support.

• Establishing controlled agent workflows that separate reasoning from execution and enforce defined boundaries around data access and automated actions.

Module 9: Retrieval-Augmented Analytics and Intelligent Querying

• Retrieval-augmented generation architectures for combining structured lakehouse data, documents, metadata, semantic information, and enterprise knowledge.

• Vector-enabled data architectures supporting semantic retrieval, similarity search, contextual discovery, and analytical knowledge access.

• Intelligent query planning involving natural-language requests, semantic interpretation, SQL generation, validation, execution, and result analysis.

• Grounding techniques for reducing unsupported analytical conclusions through evidence retrieval, source verification, and traceable reasoning workflows.

Module 10: Agent Tools, APIs and Governed Data Access

• Designing secure tools and interfaces that allow agents to interact with databases, SQL engines, APIs, catalogues, dashboards, workflows, and analytical services.

• Tool-selection and execution patterns for allowing agents to choose appropriate data services while respecting defined authorization boundaries.

• Fine-grained identity, access control, data masking, privacy policies, secrets management, and secure agent-to-data communication.

• Auditing agent actions, tool calls, data access, analytical queries, generated outputs, and automated workflow execution across enterprise environments.

Module 11: Real-Time and Event-Driven Agentic Analytics

• Integrating streaming data, event brokers, real-time processing, and continuously updated lakehouse tables with analytical agent architectures.

• Event-triggered agents for anomaly investigation, operational monitoring, alert analysis, business intelligence, and automated decision-support workflows.

• Designing low-latency analytical pipelines with event-time processing, state management, checkpointing, and fault-tolerant execution.

• Combining real-time signals with historical lakehouse data to provide agents with current conditions, trends, context, and comparative evidence.

Module 12: AI Agents for Data Engineering and Autonomous Operations

• Applying AI agents to pipeline development, data transformation, metadata enrichment, documentation, testing, and engineering workflow automation.

• Intelligent data quality agents capable of investigating anomalies, identifying potential causes, recommending remediation, and coordinating approved corrective actions.

• Autonomous workload optimization involving query analysis, resource recommendations, storage optimization, pipeline scheduling, and cost management.

• Human-supervised autonomous operations with approval gates, policy controls, audit trails, rollback mechanisms, and operational safeguards.

Module 13: Agent Evaluation, Reliability and Performance Engineering

• Evaluation frameworks for measuring analytical correctness, data grounding, reasoning quality, tool selection, query accuracy, and response usefulness.

• Designing benchmark datasets, test scenarios, golden queries, evaluation criteria, regression tests, and continuous agent quality measurement.

• Monitoring agent latency, token consumption, query execution costs, retrieval performance, infrastructure utilization, and overall analytical efficiency.

• Reliability engineering for agentic systems including failure handling, retries, fallback strategies, circuit breakers, escalation, and human intervention.

Module 14: Security, Governance, Privacy and Responsible Agentic Analytics

• Governance frameworks for controlling how analytical agents discover, access, process, interpret, and act upon enterprise information.

• Implementing policy-as-code, machine-verifiable controls, data classification, privacy restrictions, retention policies, and regulatory requirements.

• Provenance and audit architectures capturing data sources, queries, retrieved evidence, tool calls, transformations, outputs, and downstream actions.

• Human oversight, approval workflows, risk controls, model governance, agent boundaries, and secure operational practices for enterprise deployments.

Module 15: AI-Native Data Products and Intelligent Analytical Services

• Designing agent-ready data products with machine-readable metadata, semantic definitions, quality indicators, contracts, lineage, and standardized access interfaces.

• Building analytical services that expose governed metrics, business logic, forecasts, insights, and contextual information to intelligent agents and applications.

• Integrating data products with data mesh, data fabric, semantic architectures, knowledge graphs, and enterprise AI platforms.

• Product lifecycle management for AI-consumed data assets covering discovery, adoption, monitoring, versioning, improvement, and retirement.

Module 16: Enterprise AI-Native Lakehouse Strategy and Implementation

• Developing enterprise roadmaps for transforming conventional lakehouses into AI-native platforms capable of supporting scalable agentic analytics.

• Assessing platform readiness across data quality, metadata, semantics, governance, security, observability, engineering maturity, and AI capabilities.

• Establishing implementation phases covering pilot use cases, platform foundations, agent integration, governance, evaluation, scaling, and operationalization.

• Measuring enterprise outcomes through analytical productivity, data reliability, agent performance, adoption, operational efficiency, cost management, and business value.

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.