Course Introduction
The Data Lakehouse and Apache Iceberg Fundamentals Training Course provides a practical foundation for understanding modern data lakehouse architectures and the role of Apache Iceberg in building reliable, scalable, and analytics-ready data platforms. Participants explore how lakehouses combine the flexibility of data lakes with the governance, performance, and management capabilities traditionally associated with data warehouses.
This course introduces the core concepts behind Apache Iceberg, including tables, snapshots, manifests, metadata files, partitioning, schema evolution, and time travel. Participants learn how these capabilities address common challenges associated with managing large analytical datasets in cloud object storage and provide a structured foundation for reliable data engineering and analytics workloads.
The Data Lakehouse and Apache Iceberg Fundamentals Training Course also examines how Iceberg integrates with modern processing and query technologies. Participants gain an understanding of how engines such as Spark, Trino, Flink, and other compatible platforms can work with Iceberg tables to support batch processing, streaming, interactive analytics, and large-scale data transformation workflows.
Through practical discussions and architecture-focused exercises, participants learn how to design foundational lakehouse environments using Apache Iceberg. Topics include table creation, partition design, schema changes, snapshot management, data ingestion, compaction, maintenance, and operational considerations that influence performance, reliability, storage efficiency, and long-term platform sustainability.
The course also addresses emerging lakehouse requirements such as open table formats, multi-engine interoperability, cloud-native analytics, data governance, streaming ingestion, and interoperability across heterogeneous technology environments. Participants examine how Apache Iceberg can contribute to reducing platform lock-in while supporting consistent data management across different compute engines and storage architectures.
By completing the Data Lakehouse and Apache Iceberg Fundamentals Training Course, participants will be equipped to communicate effectively about lakehouse architecture, understand the fundamental mechanics of Apache Iceberg, evaluate common implementation patterns, and contribute to data platform initiatives involving open table formats. The course provides a strong foundation for progressing toward advanced Iceberg engineering, lakehouse architecture, and data platform management.
Duration
5 days
Who Should Attend
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Data engineers responsible for building, maintaining, and modernizing analytical data platforms and pipelines.
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Data architects designing lakehouse, cloud data platform, and enterprise analytics architectures.
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Analytics engineers working with large-scale datasets, transformation frameworks, and modern analytical environments.
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Database administrators transitioning from traditional warehouse technologies toward open lakehouse architectures.
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Cloud engineers supporting scalable object storage, compute platforms, and cloud-native data infrastructure.
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Data platform engineers evaluating Apache Iceberg and open table formats for enterprise data workloads.
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BI and analytics professionals seeking to understand the architecture behind modern lakehouse data environments.
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Solution architects assessing technologies for scalable, interoperable, and governed analytical data platforms.
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Technology leaders involved in data modernization, cloud migration, and analytical platform transformation initiatives.
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DevOps and platform engineering professionals supporting data infrastructure automation and operational reliability.
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Data governance professionals seeking to understand how lakehouse table formats support controlled data management.
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Technical consultants and professionals evaluating Apache Iceberg for enterprise data engineering and analytics use cases.
Course Objectives
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Develop a strong understanding of data lakehouse architecture and explain how lakehouse platforms combine data lake flexibility with structured analytical data management capabilities.
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Explain the architecture and fundamental components of Apache Iceberg, including tables, snapshots, manifests, metadata files, partition specifications, and data files.
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Understand how Apache Iceberg addresses common data lake challenges involving schema changes, partition management, concurrent operations, data consistency, and analytical reliability.
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Learn how to create, manage, query, and maintain Iceberg tables across supported processing and analytical engines in modern data platform environments.
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Evaluate partitioning strategies and table organization approaches that can improve query performance, storage efficiency, and operational manageability for analytical workloads.
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Apply fundamental Iceberg capabilities such as schema evolution, partition evolution, snapshot management, time travel, and controlled data modification in practical scenarios.
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Understand how Apache Iceberg integrates with batch and streaming workloads and how open table formats support interoperability across different compute engines.
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Identify essential maintenance activities including compaction, metadata management, snapshot expiration, orphan file handling, and other operational practices for sustainable lakehouse environments.
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Recognize important considerations around governance, security, data lifecycle management, interoperability, performance optimization, and platform reliability when adopting Iceberg.
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Build the knowledge required to participate effectively in lakehouse modernization initiatives and progress toward advanced Apache Iceberg engineering and architecture responsibilities.
Comprehensive Course Outline
Module 1: Data Lakehouse Architecture Fundamentals
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Evolution from traditional data warehouses and data lakes toward modern lakehouse architectures for unified analytical workloads.
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Core lakehouse components including object storage, metadata, compute engines, governance services, and analytical interfaces.
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Differences between data lakes, warehouses, lakehouses, and open table format architectures in enterprise environments.
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Key lakehouse design principles covering scalability, interoperability, performance, governance, and operational simplicity.
Module 2: Introduction to Apache Iceberg
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Apache Iceberg architecture, design goals, major capabilities, and its role as an open table format for data lakes.
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Fundamental Iceberg concepts including tables, snapshots, manifests, metadata files, schemas, partitions, and data files.
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How Iceberg metadata enables reliable table management and consistent analytical access across large datasets.
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Common use cases for Apache Iceberg across batch analytics, streaming pipelines, data warehousing, and lakehouse platforms.
Module 3: Iceberg Tables and Table Operations
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Creating Iceberg tables and understanding table properties, namespaces, schemas, and underlying storage organization.
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Reading and writing Iceberg tables using modern processing engines and SQL-based analytical interfaces.
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Understanding inserts, updates, deletes, merges, and other data modification operations supported by Iceberg.
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Managing table properties and operational configurations to establish consistent and maintainable table behavior.
Module 4: Iceberg Metadata, Snapshots, and Time Travel
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Understanding Iceberg metadata files, manifest lists, manifests, snapshots, and their relationships within table architecture.
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Using snapshots and time travel to query previous versions of data and support analytical reproducibility requirements.
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Exploring rollback and snapshot management concepts for recovery, auditing, testing, and controlled data lifecycle operations.
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Understanding metadata growth, snapshot accumulation, and maintenance requirements in continuously changing analytical environments.
Module 5: Schema Evolution and Partition Evolution
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Applying schema evolution capabilities to add, remove, rename, reorder, and modify fields without unnecessary data rewrites.
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Understanding partition specifications and how hidden partitioning simplifies data organization and analytical query development.
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Exploring partition evolution and strategies for adapting table layouts as workloads, data volumes, and query patterns change.
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Evaluating schema and partition design decisions for compatibility, performance, maintainability, and long-term platform flexibility.
Module 6: Apache Iceberg with Modern Data Processing Engines
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Integrating Iceberg tables with Apache Spark and understanding common batch processing and transformation workflows.
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Exploring interoperability with query and processing engines such as Trino, Flink, and other Iceberg-compatible technologies.
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Understanding engine-specific capabilities, configuration considerations, and differences when working with shared Iceberg datasets.
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Designing multi-engine analytical environments that maintain consistent access to governed and interoperable lakehouse tables.
Module 7: Data Ingestion, Streaming, and Change Management
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Designing reliable batch ingestion patterns for loading structured and semi-structured data into Iceberg tables.
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Understanding streaming ingestion concepts and the role of Iceberg in supporting continuously updated analytical datasets.
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Exploring change data capture, incremental processing, upserts, deletes, and merge patterns for evolving source systems.
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Addressing consistency, late-arriving data, concurrent writes, and operational coordination across ingestion pipelines.
Module 8: Iceberg Performance and Table Maintenance
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Understanding file sizes, data layout, partitioning, sorting, and metadata organization as factors influencing analytical performance.
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Exploring compaction, file rewriting, clustering, and optimization strategies for reducing small-file and inefficient-layout problems.
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Managing snapshots, expired metadata, orphan files, and other maintenance activities required for long-term table health.
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Evaluating query performance and operational trade-offs when optimizing Iceberg tables for changing workload patterns.
Module 9: Lakehouse Governance, Security, and Operations
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Understanding governance considerations for Iceberg-based platforms including ownership, lineage, catalogues, and data lifecycle controls.
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Exploring access management, encryption, auditing, compliance requirements, and security patterns for cloud-based lakehouse environments.
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Understanding catalogue options and how metadata services support discovery, management, interoperability, and operational consistency.
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Establishing monitoring and operational practices for detecting table health issues, ingestion failures, metadata problems, and performance degradation.
Module 10: Emerging Iceberg and Lakehouse Architecture Practices
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Evaluating open table formats, multi-engine interoperability, and their implications for reducing dependency on individual data platforms.
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Exploring modern lakehouse patterns involving streaming analytics, real-time data, AI workloads, and large-scale machine learning datasets.
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Understanding emerging considerations around catalog interoperability, REST-based catalogues, cloud-native architectures, and distributed data platforms.
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Developing practical adoption strategies for introducing Apache Iceberg into existing data lake, warehouse, and enterprise modernization initiatives.
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.
