Word count: 3500 words
Objectives to cover:
Introduction to Data Lakehouse Technologies: Introduces the evolution and significance of lakehouse architectures for unified data storage and analytics.
Overview of Automotive Data Engineering Requirements: Summarizes key needs in the automotive sector, such as real-time processing, scalability, and reliability.
Architecture and Features of Delta Lake, Iceberg, and Hudi: Highlights core design principles and capabilities of the three leading lakehouse frameworks.
Performance and Query Optimization: Compares data ingestion, query latency, and indexing strategies across the technologies.
Schema Evolution and Time Travel Support: Reviews how each system handles data versioning, updates, and historical querying.
Fault Tolerance and Consistency Mechanisms: Evaluates mechanisms for ensuring data reliability, ACID transactions, and recovery.
Integration with Automotive Data Workflows: Discusses compatibility with typical automotive pipelines such as telemetry and diagnostics.
Scalability and Real-Time Processing Use Cases: Analyzes how each platform supports high-volume data and near real-time insights.
Conclusion and Automotive Recommendations: Concludes with a summary and tailored recommendations for platform selection in automotive environments.
Reference: IEEE style