Apache Iceberg – Table Format

Module 1: Introduction to Open Table Formats

• Data lake challenges: lack of ACID, schema evolution, and partition evolution
• Open table format landscape: Apache Iceberg, Delta Lake, and Apache Hudi
• Apache Iceberg overview: design goals and key features
• Iceberg adoption: companies and query engines using Iceberg

Module 2: Iceberg Architecture and File Structure

• Iceberg table architecture: catalog, metadata, and data files
• Metadata files: table metadata JSON, manifest lists, and manifest files
• Data files: Parquet, ORC, and Avro in Iceberg tables
• Snapshot model: how Iceberg tracks table state over time

Module 3: Working with Iceberg Using Spark

• Setting up Apache Spark with Iceberg catalog integration
• Creating Iceberg tables: partitioning and sorting configuration
• Reading and writing Iceberg tables from Spark DataFrames
• MERGE INTO, UPDATE, and DELETE on Iceberg tables

Module 4: Schema and Partition Evolution

• Schema evolution: adding, renaming, and dropping columns safely
• Partition evolution: changing partition strategy without rewriting data
• Hidden partitioning: partition transforms for transparent query acceleration
• Sort order evolution: updating table sort strategy

Module 5: Time Travel and Rollback

• Snapshot queries: reading table state at a specific version or timestamp
• Rollback: reverting table to a previous snapshot state
• Audit history: listing snapshots and metadata log entries
• Expire snapshots and remove orphan files for storage management

Module 6: Iceberg with Different Query Engines

• Iceberg with Trino and Presto: query configuration and performance
• Iceberg with Hive: reading and writing Iceberg tables from HiveQL
• Iceberg with Flink: streaming writes and CDC with Iceberg sinks
• Iceberg with Dremio and StarRocks: analytical query acceleration

Module 7: Iceberg Catalog Management

• Catalog types: Hive Metastore, AWS Glue, REST, and JDBC catalogs
• REST catalog: the emerging standard for cross-engine Iceberg access
• Nessie catalog: Git-like version control for data lakehouse tables
• Multi-engine access patterns and catalog federation

Module 8: Capstone Project and Assessment

• Iceberg table creation and data ingestion pipeline with Spark
• Schema and partition evolution exercise with data validation
• Time travel query and rollback simulation project
• Final assessment and course certification

Experience in the Industry Gain expertise from data lakehouse engineers who have implemented Apache Iceberg as the open table format for cloud data lakes on AWS, Azure, and GCP, enabling reliable analytics across multiple query engines.

Backgrounds at the Top Our Apache Iceberg trainers have built open lakehouse architectures at technology companies and data engineering teams adopting Iceberg for its ACID guarantees, schema evolution, and multi-engine compatibility.

Clear & Effective Teaching Iceberg architecture, snapshot model, schema and partition evolution, time travel, Spark integration, catalog management, and multi-engine access are explained with real data lake modernisation examples.

Hands-On Learning Focus Students create Iceberg tables with Spark, perform schema and partition evolution, run time travel queries, manage catalog configurations, and build streaming Flink pipelines through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Apache Iceberg releases, Iceberg REST catalog standard adoption, Iceberg v3 format features, and evolving open table format best practices.