Course Introduction
The Modern Data Lakehouse Architecture Training Course provides a comprehensive understanding of how organizations can design and operate modern data platforms that combine the flexibility of data lakes with the reliability, performance, governance, and analytical capabilities traditionally associated with data warehouses. Participants explore lakehouse principles, architecture patterns, storage layers, processing engines, metadata, governance, and modern analytical workloads.
The course examines the architectural foundations of data lakehouses and how they support structured, semi-structured, and unstructured data within unified environments. Participants learn how cloud object storage, open table formats, distributed processing, metadata management, transactional capabilities, and analytical engines can work together to create scalable platforms for business intelligence, advanced analytics, machine learning, artificial intelligence, and operational data use cases.
Participants will explore practical data engineering and integration patterns for ingesting data from databases, applications, APIs, files, streaming systems, and external sources. The programme addresses batch and real-time ingestion, transformation, orchestration, change data capture, data pipelines, data quality, schema evolution, workload management, and performance optimization while considering the requirements of increasingly diverse and rapidly changing data environments.
The programme also addresses governance, security, reliability, and operational management within lakehouse architectures. Participants examine metadata, catalogues, lineage, access controls, encryption, privacy, data quality, observability, retention, lifecycle management, and regulatory requirements. These capabilities are considered across cloud, hybrid, multi-cloud, and enterprise environments where data must remain discoverable, trustworthy, secure, and efficiently accessible.
Emerging technologies and architectural developments are incorporated throughout the course, including AI-ready lakehouses, vector data, machine learning workloads, generative AI, semantic layers, data sharing, streaming analytics, intelligent metadata, and automated data management. Participants consider how lakehouse platforms can support modern AI and analytics requirements while addressing issues such as data quality, cost management, model governance, security, interoperability, and responsible data use.
By the end of the programme, participants will be equipped to evaluate, design, implement, and optimize modern data lakehouse architectures. They will gain practical frameworks for selecting architectural components, designing reliable data pipelines, managing workloads, implementing governance, improving performance, and building scalable data platforms that support analytics, artificial intelligence, reporting, and enterprise data-driven initiatives.
Duration
5 days
Who Should Attend
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Data architects responsible for designing modern lakehouse, cloud data platform, and enterprise analytical architectures.
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Enterprise architects developing strategies for data modernization, cloud adoption, analytics platforms, and distributed data environments.
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Data engineers building ingestion pipelines, transformation workflows, data processing systems, and lakehouse data platforms.
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Cloud architects responsible for designing scalable, secure, resilient, and cost-effective cloud data infrastructure.
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Data platform engineers managing storage, compute, metadata, orchestration, security, and operational services.
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Data governance professionals responsible for metadata, lineage, quality, access controls, privacy, stewardship, and regulatory requirements.
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Analytics and business intelligence professionals working with modern analytical platforms and large-scale enterprise datasets.
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Machine learning and AI professionals requiring reliable, scalable, governed data environments for model development and deployment.
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Database administrators and technology specialists transitioning traditional warehouse environments toward modern lakehouse architectures.
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Chief Data Officers and data management leaders developing enterprise data platform modernization strategies.
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Technology managers evaluating lakehouse platforms, open table formats, cloud services, processing engines, and analytical technologies.
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Digital transformation leaders seeking modern architectures that unify data engineering, analytics, machine learning, and artificial intelligence workloads.
Course Objectives
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Develop a comprehensive understanding of modern data lakehouse principles, architecture components, capabilities, use cases, and their role in enterprise data modernization.
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Explain how object storage, open table formats, metadata services, processing engines, analytical tools, and governance capabilities combine within lakehouse environments.
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Design scalable lakehouse architectures that support structured, semi-structured, and unstructured data across batch, streaming, analytical, machine learning, and AI workloads.
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Apply appropriate ingestion and integration patterns for databases, applications, APIs, files, streaming platforms, external sources, and changing enterprise data environments.
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Establish effective data organization strategies using zones, layers, schemas, partitioning, table structures, metadata, lifecycle policies, and workload-specific design principles.
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Implement governance, security, privacy, lineage, quality, access management, and compliance capabilities that protect and improve the reliability of lakehouse data.
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Optimize lakehouse performance through appropriate storage layouts, partitioning, indexing, caching, compute selection, workload management, query optimization, and resource allocation.
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Examine emerging lakehouse capabilities for artificial intelligence, machine learning, vector data, generative AI, semantic analytics, real-time processing, and advanced data applications.
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Establish operational practices for monitoring data pipelines, storage, compute, quality, costs, performance, availability, failures, and overall lakehouse platform health.
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Develop an actionable lakehouse modernization roadmap that aligns architecture, technology, governance, skills, operating models, investment priorities, and measurable business outcomes.
Comprehensive Course Outline
Module 1: Foundations of Modern Data Lakehouse Architecture
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Understanding data lakehouse concepts, characteristics, architectural principles, use cases, and evolution within modern enterprise data platforms.
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Comparing data lakes, data warehouses, lakehouses, data fabrics, and hybrid analytical architectures across technical and business requirements.
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Examining how lakehouses support analytics, reporting, machine learning, artificial intelligence, data science, and operational data workloads.
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Identifying architectural challenges involving scalability, governance, performance, cost, interoperability, data quality, and platform complexity.
Module 2: Lakehouse Architecture and Core Components
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Designing logical and physical lakehouse architectures covering storage, compute, metadata, processing, orchestration, governance, and consumption layers.
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Understanding the role of cloud object storage, distributed processing engines, analytical services, catalogues, and open table technologies.
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Establishing architecture patterns for centralized, federated, hybrid, multi-cloud, and domain-oriented lakehouse environments.
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Aligning lakehouse architecture with enterprise architecture standards, business requirements, technology strategies, and data modernization objectives.
Module 3: Data Storage, Tables, and Open Data Formats
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Designing storage structures for structured, semi-structured, and unstructured data using appropriate file, table, and object storage patterns.
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Understanding open table formats, transactional capabilities, schema enforcement, schema evolution, versioning, and time-travel functionality.
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Applying partitioning, clustering, compaction, file management, and storage optimization techniques for large-scale analytical environments.
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Establishing data organization strategies that balance performance, maintainability, interoperability, cost, and ease of consumption.
Module 4: Data Ingestion and Integration Pipelines
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Designing batch, micro-batch, streaming, API-based, and change-data-capture approaches for ingesting diverse enterprise data sources.
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Building reliable pipelines for databases, applications, files, APIs, event streams, SaaS platforms, and external information sources.
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Managing schema changes, late-arriving data, duplicates, failed records, data drift, dependencies, and pipeline recovery requirements.
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Applying orchestration, automation, testing, monitoring, and deployment practices to improve the reliability of lakehouse data pipelines.
Module 5: Data Processing, Transformation, and Modeling
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Designing transformation workflows that convert raw data into validated, standardized, enriched, and analytics-ready lakehouse datasets.
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Applying batch and distributed processing approaches for large-scale transformation, aggregation, enrichment, and analytical workloads.
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Developing appropriate data modeling strategies for reporting, business intelligence, data science, machine learning, and operational use cases.
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Managing reusable transformation logic, dependencies, testing, version control, deployment processes, and maintainability across data engineering environments.
Module 6: Governance, Metadata, Quality, and Lineage
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Establishing metadata management practices that support discovery, documentation, ownership, classification, relationships, and effective lakehouse governance.
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Implementing data quality controls covering accuracy, completeness, consistency, validity, timeliness, freshness, and business rule compliance.
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Designing lineage and impact analysis capabilities that connect source systems, transformations, tables, models, reports, and downstream data products.
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Integrating governance policies, stewardship responsibilities, certification processes, retention requirements, and lifecycle controls into lakehouse operations.
Module 7: Security, Privacy, and Access Management
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Designing lakehouse security architectures covering identity, authentication, authorization, encryption, network controls, secrets, and secure data movement.
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Applying role-based and attribute-based access controls to datasets, tables, columns, files, analytical assets, and sensitive information.
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Managing privacy requirements for personal, confidential, regulated, and commercially sensitive information within large-scale data environments.
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Establishing auditing, monitoring, access reviews, policy enforcement, and compliance controls across cloud and hybrid lakehouse platforms.
Module 8: Performance, Scalability, and Cost Optimization
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Optimizing query performance through partitioning, clustering, indexing, caching, storage layout, file management, and workload-specific techniques.
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Selecting appropriate compute resources and processing architectures based on workload characteristics, concurrency, latency, scale, and business requirements.
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Establishing workload management strategies that balance interactive analytics, scheduled processing, streaming, data science, and machine learning workloads.
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Applying cost management practices for storage, compute, data movement, processing, retention, and resource utilization across cloud lakehouse environments.
Module 9: AI, Machine Learning, Streaming, and Advanced Lakehouse Capabilities
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Designing lakehouse environments that support machine learning, generative AI, vector data, feature engineering, model development, and advanced analytical workloads.
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Integrating real-time streaming and event-driven processing capabilities to support timely analytics, monitoring, personalization, and operational intelligence.
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Exploring semantic layers, intelligent metadata, AI-assisted data engineering, automated quality management, and natural-language analytical experiences.
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Addressing emerging requirements for AI-ready data including governance, lineage, quality, security, model context, data preparation, and responsible data usage.
Module 10: Lakehouse Implementation, Operations, and Future Trends
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Developing phased implementation roadmaps covering architecture, platform selection, migration, integration, governance, skills, operations, and organizational change.
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Establishing observability practices for pipelines, storage, compute, data quality, performance, availability, incidents, dependencies, and platform health.
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Exploring emerging developments including open lakehouse ecosystems, real-time lakehouses, intelligent data platforms, AI-native architectures, and automated operations.
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Creating an enterprise action plan that connects lakehouse modernization with analytics, artificial intelligence, data governance, digital transformation, and strategic 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.
