Advanced Apache Iceberg

• Familiarity with core concepts, basic operations, and Iceberg table management

Audience

• Data engineers
• Data architects
• Data analysts
• Software developers

Apache Iceberg is an open-source table format for large-scale data sets that brings the reliability and simplicity of SQL tables to big data. It was designed to solve the challenges of managing big data in data lakes, which often involve handling complex schemas, large files, and diverse data sources.

This instructor-led, live training (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

By the end of this training, participants will be able to:

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Apache Iceberg

• Overview of Apache Iceberg
• Review of basic concepts

Deep Dive into Iceberg Architecture

• In-depth analysis of Iceberg's table format
• Detailed architecture overview, including metadata and file layout
• Internals of schema and partition evolution​

Advanced Installation and Configuration

• Configuring Iceberg for optimal performance in different environments
• Integration with various data processing engines
• Advanced setup: security, encryption, and access controls
• Setting up Iceberg in a distributed environment

Advanced Operations and Maintenance

• Managing large-scale Iceberg tables
• Implementing and managing complex schema changes
• Handling partition evolution and hidden partitioning
• Advanced CRUD operations with schema and partition changes

Query Optimization Techniques

• Techniques for reducing query latency
• Partition pruning and file pruning
• Metadata caching and optimization strategies
• Implementing and testing query optimization techniques​

Performance Tuning for Large Datasets

• Optimizing performance for large-scale datasets
• Using Iceberg's built-in features for performance tuning
• Case studies on performance tuning in real-world scenarios
• Tuning performance for large-scale datasets

Advanced Data Migration and Integration

• Migrating complex data structures from other systems
• Integrating Iceberg with real-time data streams
• Migrating complex datasets and integrating real-time data streams​

Reliability and Consistency

• Ensuring data consistency and integrity in distributed environments
• Implementing and managing transactional guarantees
• Handling failures and recovery mechanisms
• Implementing reliability and consistency features​

Advanced Features and Customization

• Custom catalog implementations
• Extending Iceberg with custom features
• Implementing custom catalog and extending Iceberg functionalities​

Data Governance and Compliance

• Implementing data governance policies
• Compliance with data regulations
• Managing audit trails and data lineage
• Implementing governance and compliance features​

Summary and Next Steps