This module explores the transactional capabilities of lakehouse systems, focusing on ACID properties, conflict resolution mechanisms, and how these systems ensure data integrity under high concurrency. Learners will gain an understanding of how modern data architectures handle complex transactional operations and maintain reliability.

This module provides a comprehensive exploration of Apache Iceberg's architecture, including its metadata and data layers, unique capabilities, and integration with compute engines like Spark and Flink. Learners will gain an understanding of how Iceberg enhances data lakehouse performance and manageability through hands-on exercises and practical implementation examples.

This module provides a comprehensive overview of Apache Hudi, focusing on its architecture, data layer design, catalog integration, and performance optimization techniques. Learners will gain an understanding of how Hudi enhances data lake capabilities through transactional operations, efficient data management, and integration with compute engines like Spark and Flink.

This module provides an in-depth exploration of Delta Lake's architecture, transaction log protocol, and advanced features such as ACID compliance, time travel, and row-level operations. Learners will gain practical knowledge of how Delta Lake ensures data reliability and performance in lakehouse environments, along with hands-on experience integrating it with Apache Spark and Flink.

This module explores the critical role of catalogs and metadata management in lakehouse architectures, covering key concepts like Iceberg REST catalogs, metadata governance, and the benefits of using modern catalog solutions. Learners will gain insights into how catalogs enable scalable data operations and support diverse table formats.

This module explores the challenges and solutions for interoperability in lakehouse architectures, focusing on Apache XTable and Delta UniForm. Learners will gain an understanding of how these tools enable seamless data operations across open table formats like Apache Hudi, Iceberg, and Delta Lake. The module also covers practical implementation using Apache Spark.

This module provides an in-depth look at performance optimization techniques in lakehouse architectures, including storage strategies like partitioning and compaction, query engine improvements, and practical tuning approaches to enhance system efficiency and reduce costs.

This module delves into the essential aspects of data governance and security within lakehouse environments, covering frameworks for data quality, access controls, compliance, and monitoring. Learners will gain insights into how open table formats like Apache Iceberg and Delta Lake support governance practices. The module equips students with the knowledge to implement secure and compliant data management strategies.

This module explores the critical factors in evaluating and selecting open table formats, including workload patterns, ecosystem compatibility, and versioning capabilities. Learners will gain insights into how different formats like Apache Iceberg, Hudi, and Delta Lake align with specific use cases and data processing needs. The module also addresses real-world implementation challenges and decision-making strategies.

This module explores practical implementations of data architecture solutions using real-world scenarios, focusing on scalable data pipelines, real-time analytics, and machine learning workflows. Learners will gain hands-on understanding of technologies like Apache Hudi, Delta Lake, and MLflow to address complex data challenges in enterprise settings.