Apache Iceberg Training is a practical, hands-on program designed to equip learners with the skills required to work with modern open table formats for large-scale data lakes. This training focuses on Apache Iceberg architecture, table design, schema evolution, partitioning strategies, time travel, data versioning, and integration with big data engines such as Spark, Hive, and Flink. Participants will gain real-world experience in building reliable, scalable, and high-performance data lakehouse solutions for analytics and data engineering workloads.

Participants will gain strong practical expertise in Apache Iceberg, enabling them to design modern data lake architectures, manage evolving datasets, and optimize large-scale analytics workloads.

• Data Engineers and Analytics Engineers
• Big Data Developers and Architects
• Data Platform and Lakehouse Engineers
• BI Professionals working with large datasets
• IT Professionals transitioning into modern data lake architectures

• Basic understanding of SQL and data warehousing concepts
• Familiarity with Apache Spark or Hadoop ecosystem basics is helpful
• Understanding of data processing and ETL concepts
• Basic knowledge of distributed systems

• Apache Iceberg architecture and table format understanding
• Data lakehouse design principles
• Schema evolution and metadata management
• Partitioning and query optimization strategies
• Time travel and data versioning concepts
• Integration with Spark, Hive, and Flink
• Performance tuning for large-scale datasets

Upon completion of this training, participants will be able to design and manage modern data lakehouse solutions using Apache Iceberg. They will be capable of building scalable, versioned, and high-performance data tables for analytics and big data processing.

Introduction to Apache Iceberg and Lakehouse Architecture
• Overview of data lakes vs lakehouse architecture
• Apache Iceberg fundamentals and components
• Table format structure and metadata layers
• Setting up Iceberg environment with Spark/Hadoop

Iceberg Table Design and Data Modeling
• Creating and managing Iceberg tables
• Schema design and evolution techniques
• Partitioning strategies for performance optimization
• Writing and reading data using Iceberg
Advanced Iceberg Features and Data Management
• Time travel and snapshot management
• Data versioning and rollback mechanisms
• Query optimization and metadata pruning
• Handling large-scale datasets efficiently

Integration and Production Best Practices
• Integration with Spark, Hive, and Flink
• Catalog management and table maintenance
• Performance tuning strategies
• Best practices for production lakehouse environments

• Iceberg architecture and metadata management
• Table design and schema evolution
• Partitioning and query optimization
• Time travel and versioning
• Integration with big data engines
• Lakehouse architecture concepts

• Hands-on Iceberg table creation exercises
• Schema evolution and time travel tasks
• Practical Spark integration assignments
• Mini project on lakehouse data pipeline design

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Apache Iceberg Training, validating their expertise in lakehouse architecture, open table formats, data versioning, and scalable big data processing using Apache Iceberg.