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Course Outline
Introduction to Apache Iceberg
- Overview of Apache Iceberg
- Importance and use cases in modern data architecture
- Key features and benefits
Core Concepts
- Iceberg table format and architecture
- Comparison with other table formats
- Partitioning and schema evolution
- Time travel and data versioning
Setting Up Apache Iceberg
- Installation and configuration
- Integrating Iceberg with various data processing engines
- Setting up an Iceberg environment on a local machine
Basic Operations
- Creating and managing Iceberg tables
- Writing to and reading from Iceberg tables
- Basic CRUD operations
Data Migration and Integration
- Migrating data from Hive and other systems to Iceberg
- Integration with BI tools
- Migrating a sample dataset to Iceberg
Optimizing Performance
- Performance tuning techniques
- Optimizing queries and data scans
- Performance optimization in Iceberg
Overview of Advanced Features
- Partition evolution and hidden partitioning
- Table evolution and schema changes
- Time travel and rollback features
- Implementing advanced features in Iceberg
Summary and Next Steps
Requirements
- Familiarity with concepts such as tables, schemas, partitions, and data ingestion
- Basic knowledge of SQL
Audience
- Data engineers
- Data architects
- Data analysts
- Software developers
14 Hours
Testimonials (3)
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data
very interactive...
