By completing this course, learners will:

• Deploy and configure Apache Iceberg.

• Create and manage Iceberg tables.

• Implement schema evolution and partitioning.

• Use time travel and rollback features.

• Integrate Iceberg with modern query engines.

• Operate Iceberg at scale in a data lakehouse environment.

Course Syllabus

Module 1: Introduction to Apache Iceberg

• What is Apache Iceberg?

• Iceberg vs Hive/Delta/Parquet tables

• Installing and setting up Iceberg

Module 2: Core Architecture

• Table format design

• Metadata layers (snapshots, manifests)

• ACID transactions in Iceberg

• Partitioning strategies

Module 3: Table Operations

• Creating Iceberg tables

• Inserting and updating data

• Deletes and incremental updates

• Time travel and rollback

Module 4: Schema Evolution

• Adding, renaming, and deleting columns

• Partition evolution

• Managing table versions

• Handling large-scale schema changes

Module 5: Query Engines Integration

• Iceberg with Apache Spark

• Iceberg with Apache Flink

• Iceberg with Trino and Presto

• SQL queries and analytics

Module 6: Cloud & Storage Integration

• Iceberg with Amazon S3

• Iceberg with Google Cloud Storage (GCS)

• Iceberg with Azure Data Lake (ADLS)

• On-premises Hadoop compatibility

Module 7: Deployment & Scaling

• Deploying Iceberg in production

• Optimizing query performance

• Compaction and garbage collection

• Monitoring and observability

Module 8: Real-World Projects

• Building a data lakehouse with Iceberg and Spark

• Streaming ingestion with Flink + Iceberg

• Time travel analytics for business intelligence

• Multi-engine queries with Trino + Iceberg

Module 9: Best Practices & Future Trends

• Iceberg vs Delta Lake vs Apache Hudi

• Cost optimization strategies

• Data governance and compliance

• The future of open table formats

Apache Iceberg skills prepare learners for roles such as:

• Data Engineer (big data pipelines)

• Analytics Engineer (BI + data lakes)

• Cloud Data Engineer (AWS/GCP/Azure)

• Big Data Architect (lakehouse systems)

• Machine Learning Engineer (data preparation at scale)

Iceberg is being rapidly adopted by companies like Netflix, Apple, and Adobe to power modern data platforms, making it a valuable and in-demand skill.

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Apache Iceberg and modern data lakehouse technologies. This certification demonstrates readiness for roles in data engineering, analytics, and big data platform development.

1. What is Apache Iceberg?
An open table format for large-scale analytics datasets, enabling schema evolution, time travel, and ACID transactions in data lakes.

2. How does Iceberg differ from Hive tables?
Iceberg supports schema evolution, partitioning, and ACID operations, while Hive tables are rigid and lack transactional reliability.

3. What are Iceberg’s key features?

• Schema evolution

• Time travel queries

• Hidden partitioning

• ACID transactions

4. What query engines support Iceberg?
Spark, Flink, Trino, Presto, and Hive.

5. What is time travel in Iceberg?
The ability to query past versions of a dataset using snapshots.

6. How does Iceberg achieve ACID transactions?
Through atomic snapshot replacement and metadata layers that track changes safely.

7. What storage systems work with Iceberg?
Amazon S3, Google Cloud Storage, Azure Data Lake, and HDFS.

8. What are the benefits of Iceberg?

• Reliability at scale

• Performance with big data

• Compatibility with multiple engines

• Open-source, vendor-neutral

9. What are challenges with Iceberg?

• Complex setup compared to Hive

• Relatively new ecosystem

• Requires expertise in Spark/Flink integration

10. Where is Apache Iceberg being adopted?
By enterprises and tech leaders like Netflix, Apple, Adobe, and others modernizing their data lakehouse architectures.