Open Lakehouse Architecture with Apache Iceberg Training Course

Course Introduction

Open Lakehouse Architecture with Apache Iceberg provides enterprises with a modern approach to building scalable, interoperable, and vendor-neutral analytical data platforms. This advanced training course explores how Apache Iceberg can provide a reliable table abstraction over cloud and distributed object storage while supporting transactional consistency, schema evolution, partition evolution, time travel, concurrent workloads, and large-scale analytical processing. Participants will examine how open lakehouse principles can modernize enterprise data architectures without creating unnecessary dependence on proprietary storage and processing ecosystems.

The course provides comprehensive coverage of Apache Iceberg architecture, table structures, metadata management, snapshots, manifests, catalogs, schema evolution, partitioning, transactions, and data lifecycle capabilities. Participants will develop a detailed understanding of how Iceberg manages large analytical datasets and enables reliable table operations across distributed environments. The program also examines how Iceberg integrates with SQL engines, processing frameworks, catalog services, streaming platforms, orchestration tools, and cloud-native infrastructure to create flexible enterprise lakehouse architectures.

A major focus is placed on engineering reliable data pipelines using Apache Iceberg for both batch and streaming workloads. Participants will explore ingestion, incremental processing, change data capture, merge operations, upserts, deletes, compaction, snapshot management, write optimization, and streaming table maintenance. The course addresses practical engineering challenges associated with high-volume data, concurrent writers, schema changes, late-arriving records, evolving business requirements, and continuously updated analytical datasets.

The program also examines open lakehouse governance, security, metadata, lineage, quality, observability, and interoperability. Participants will learn how to combine Iceberg with enterprise catalogues, identity and access management, data classification, encryption, auditing, data contracts, and automated policy controls. Special attention is given to maintaining governance across multi-engine environments where different analytical and processing technologies interact with the same open tables, enabling organizations to achieve both platform flexibility and consistent enterprise controls.

Participants will investigate advanced Apache Iceberg use cases involving data mesh, data products, real-time analytics, machine learning, generative AI, and multi-cloud architectures. The course explores how open tables can become reusable data foundations for analytical products and AI workloads while preserving versioned, reproducible, and governed data. Advanced concepts include Iceberg REST catalogues, branching and tagging, metadata optimization, streaming architectures, engine interoperability, lakehouse federation, and integration with semantic and AI-oriented data services.

By the end of the training, participants will be equipped to design, implement, govern, optimize, and operate enterprise lakehouse architectures using Apache Iceberg and complementary open technologies. The course provides practical frameworks for platform selection, migration planning, workload optimization, cost management, interoperability, reliability engineering, and long-term lakehouse evolution. Participants will gain the knowledge required to build open analytical environments capable of supporting diverse workloads while reducing architectural constraints and improving flexibility across enterprise data ecosystems.

Duration

10 days

Who Should Attend

• Enterprise Data Architects designing open, scalable, and interoperable lakehouse architectures.

• Lakehouse Architects responsible for implementing Apache Iceberg-based analytical data platforms.

• Data Engineers building batch, streaming, incremental, and transactional data pipelines.

• Data Platform Engineers developing shared infrastructure, catalogues, processing services, and self-service capabilities.

• Cloud Architects designing cloud-native and multi-cloud data architectures using open storage and table technologies.

• Analytics Engineers developing reliable analytical models and datasets on open lakehouse platforms.

• Data Warehouse Architects modernizing proprietary warehouse environments toward open lakehouse architectures.

• Data Scientists and Machine Learning Engineers requiring versioned, reliable, and scalable data foundations.

• MLOps Professionals integrating machine learning workflows with governed Iceberg data environments.

• Data Governance Professionals managing metadata, lineage, quality, security, access, and compliance across open platforms.

• Data Quality and Observability Professionals responsible for monitoring data reliability and lakehouse operations.

• Streaming Data Engineers implementing continuously updated analytical datasets and real-time lakehouse pipelines.

• Enterprise Architects evaluating interoperability, portability, multi-engine, and multi-cloud data platform strategies.

• Technology Managers overseeing lakehouse modernization, migration, and open data platform initiatives.

• Security, Risk, and Compliance Professionals responsible for governing enterprise lakehouse data and access controls.

Course Objectives

• Explain Apache Iceberg architecture and the principles of open lakehouse design, including how Iceberg manages tables, metadata, snapshots, manifests, and data files.

• Design enterprise lakehouse architectures using Apache Iceberg across cloud object storage, distributed compute, SQL engines, streaming systems, and catalog services.

• Implement reliable Iceberg tables supporting transactional operations, concurrent workloads, schema evolution, partition evolution, time travel, and reproducible analytical processing.

• Engineer batch and streaming ingestion pipelines that continuously create, update, merge, and maintain large-scale Iceberg datasets across diverse enterprise source systems.

• Apply advanced schema and partition management techniques to support evolving business requirements while maintaining query performance and operational stability.

• Implement Iceberg catalog architectures using appropriate catalog services, namespaces, table discovery mechanisms, access controls, and enterprise metadata integration.

• Optimize Iceberg table performance through partitioning, file sizing, compaction, clustering, metadata management, query planning, and storage lifecycle techniques.

• Establish data quality, observability, lineage, and governance capabilities that provide measurable trust and operational visibility across Iceberg-based environments.

• Integrate Apache Iceberg with distributed processing engines, SQL query platforms, orchestration frameworks, streaming technologies, and cloud-native data services.

• Apply advanced Iceberg capabilities such as branching, tagging, snapshot management, row-level operations, schema evolution, partition evolution, and metadata optimization.

• Design open lakehouse environments supporting data mesh, data products, machine learning, generative AI, real-time analytics, and multi-cloud enterprise workloads.

• Develop implementation, migration, optimization, governance, and operating strategies for establishing sustainable Apache Iceberg-based enterprise lakehouse platforms.

Comprehensive Course Outline

Module 1: Foundations of Open Lakehouse Architecture

• Evolution from traditional data warehouses and data lakes toward open lakehouse architectures based on interoperable storage and table abstractions.

• Core principles of open data platforms, including storage-compute separation, open formats, engine interoperability, portability, and scalable object storage.

• Role of Apache Iceberg within modern lakehouse architectures and comparison with traditional file-based analytical data management approaches.

• Enterprise use cases for open lakehouses across analytics, data science, machine learning, operational intelligence, and AI workloads.

Module 2: Apache Iceberg Architecture and Core Concepts

• Apache Iceberg table architecture including data files, manifest files, manifest lists, snapshots, metadata files, and table properties.

• Understanding how Iceberg abstracts physical data organization while providing transactional and analytical table management capabilities.

• Snapshot-based table state, metadata evolution, atomic operations, isolation, and consistency mechanisms across distributed environments.

• Iceberg architectural components and their relationships with storage systems, compute engines, catalogues, and enterprise data platforms.

Module 3: Iceberg Tables, Schemas and Data Modeling

• Designing Iceberg tables for structured and semi-structured analytical workloads using appropriate schema and column definitions.

• Schema evolution capabilities including column additions, removals, renaming, reordering, type changes, and compatibility considerations.

• Data modeling strategies for analytical workloads involving fact tables, dimensions, event data, historical datasets, and wide analytical structures.

• Managing nested structures, complex data types, identifiers, nullability, defaults, and evolving enterprise information requirements.

Module 4: Iceberg Partitioning and Partition Evolution

• Designing effective Iceberg partition strategies using partition transforms and workload-aware data organization principles.

• Hidden partitioning and the separation of logical table design from physical partition implementation.

• Partition evolution techniques that allow organizations to change partition strategies without rewriting historical datasets unnecessarily.

• Evaluating partition design through query patterns, data distribution, cardinality, maintenance requirements, and long-term scalability.

Module 5: Iceberg Catalogues and Metadata Management

• Apache Iceberg catalogue concepts and approaches for managing namespaces, table discovery, metadata references, and enterprise data organization.

• Integrating Iceberg with catalogue services and open catalogue architectures for centralized or distributed table management.

• REST-based catalogue patterns, metadata access, table registration, authentication, authorization, and enterprise integration considerations.

• Metadata lifecycle management, catalogue governance, discovery, lineage integration, and operational metadata management.

Module 6: Batch Ingestion and Transactional Data Engineering

• Designing batch ingestion pipelines for databases, files, APIs, enterprise applications, SaaS systems, and external data sources.

• Transactional write patterns including append, overwrite, merge, update, and delete operations for analytical data management.

• Change data capture and incremental processing strategies for continuously synchronizing operational systems with Iceberg tables.

• Error handling, retries, idempotency, checkpointing, validation, and recovery patterns for reliable production ingestion.

Module 7: Streaming and Real-Time Iceberg Architecture

• Integrating Apache Iceberg with streaming platforms and distributed processing engines for continuously updated analytical datasets.

• Streaming writes, checkpoints, micro-batching, event-time processing, late-arriving data, and state management considerations.

• Real-time ingestion architectures supporting operational analytics, monitoring, fraud detection, personalization, and event-driven decision systems.

• Managing streaming table maintenance, snapshot accumulation, compaction, latency, throughput, and downstream analytical consumption.

Module 8: Transactions, Snapshots, Branching and Version Management

• Understanding Iceberg transaction semantics, atomic commits, snapshot isolation, optimistic concurrency, and reliable multi-writer operations.

• Time travel and historical data access for auditing, reproducibility, debugging, regulatory analysis, and analytical experimentation.

• Branching and tagging strategies for development, testing, experimentation, controlled releases, and reproducible data workflows.

• Snapshot lifecycle management, retention, expiration, rollback, recovery, and operational governance of historical table states.

Module 9: Iceberg Performance Engineering and Table Optimization

• Query performance optimization through effective partitioning, file sizing, metadata planning, statistics, pruning, and workload-aware table design.

• Compaction and rewrite strategies for reducing small files, improving scan efficiency, and maintaining healthy analytical table structures.

• Clustering and data organization approaches for improving query performance across high-volume and frequently accessed datasets.

• Performance monitoring, benchmarking, bottleneck identification, capacity planning, and continuous optimization of Iceberg workloads.

Module 10: Data Quality, Observability and Reliability

• Implementing automated data quality checks covering completeness, accuracy, consistency, validity, uniqueness, freshness, and business-rule compliance.

• Observability architectures for monitoring ingestion, table health, snapshot activity, schema changes, data distributions, query behavior, and platform performance.

• Data lineage and dependency tracking across source systems, Iceberg tables, transformations, analytical models, and downstream consumers.

• Incident management, root-cause analysis, remediation, reliability engineering, and continuous improvement for production lakehouse environments.

Module 11: Governance, Security and Compliance for Open Lakehouses

• Designing governance frameworks for ownership, classification, access management, retention, privacy, data quality, and regulatory compliance.

• Integrating identity and access management with Iceberg catalogues, storage layers, processing engines, and analytical consumption platforms.

• Encryption, masking, secure sharing, authorization, audit logging, and policy enforcement across distributed open lakehouse environments.

• Policy-as-code and automated governance approaches for maintaining consistent controls across multi-engine and multi-cloud Iceberg platforms.

Module 12: Apache Iceberg and Multi-Engine Interoperability

• Integrating Iceberg tables with multiple SQL, processing, analytics, and data engineering engines without duplicating underlying datasets.

• Architectural considerations for interoperability, engine-specific capabilities, SQL compatibility, transaction behavior, and feature support.

• Designing shared open tables that serve diverse workloads while preserving consistent semantics, governance, and operational expectations.

• Managing engine compatibility, versioning, testing, performance differences, and operational standards across heterogeneous analytical ecosystems.

Module 13: Iceberg for Data Mesh, Data Products and AI

• Designing Iceberg-based data products that provide reusable, governed, versioned, and interoperable datasets for enterprise consumers.

• Integrating Iceberg with data mesh architectures through domain ownership, product contracts, metadata, governance, and self-service data platforms.

• Supporting machine learning and AI workloads with reproducible datasets, historical snapshots, governed access, and reliable data pipelines.

• Designing AI-ready lakehouse foundations incorporating semantic metadata, vector-enabled data, knowledge systems, and analytical services.

Module 14: Cloud, Hybrid and Multi-Cloud Iceberg Architectures

• Designing cloud-native Iceberg environments using scalable object storage, distributed compute, managed catalogues, and cloud data services.

• Hybrid architectures connecting on-premises systems with cloud-based Iceberg tables while managing security, latency, connectivity, and data movement.

• Multi-cloud strategies focused on interoperability, portability, resilience, workload placement, governance, and avoidance of unnecessary platform lock-in.

• Cloud financial management covering storage lifecycle, compute utilization, data transfer, workload scheduling, and operational cost optimization.

Module 15: Advanced Iceberg Engineering and Emerging Capabilities

• Advanced table management techniques involving metadata optimization, row-level operations, table maintenance, snapshot management, and workload-aware engineering.

• Automated Iceberg operations using orchestration, infrastructure-as-code, CI/CD, DataOps, monitoring, and intelligent maintenance workflows.

• Integration of Iceberg with modern data fabric, semantic layer, active metadata, real-time analytics, and intelligent data platform architectures.

• Emerging open lakehouse practices involving AI-assisted engineering, autonomous optimization, intelligent governance, and increasingly interoperable data ecosystems.

Module 16: Enterprise Apache Iceberg Implementation and Migration Strategy

• Developing enterprise strategies for adopting Apache Iceberg across existing warehouses, data lakes, analytical platforms, and fragmented data environments.

• Migration planning covering workload assessment, table conversion, compatibility, data validation, cutover strategies, rollback planning, and consumer transition.

• Establishing operating models for platform engineering, table ownership, governance, maintenance, quality management, support, and lifecycle management.

• Measuring implementation success through interoperability, performance, reliability, adoption, cost efficiency, engineering productivity, governance maturity, 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.