✨ AI based Iceberg Table Management

Overview

AI-Based Iceberg Table Management introduces intelligent, automated optimization and governance for Apache Iceberg tables within the IOMETE Lakehouse Platform.

This capability leverages machine learning and AI-driven heuristics to analyze table usage, data layout, and query performance — continuously optimizing compaction, partitioning, and metadata operations without manual tuning.

The result is a self-optimizing data layer that improves performance, reduces cost, and simplifies lifecycle management at scale, while maintaining full compatibility with IOMETE’s governance and domain isolation principles.

Planned Features

• Smart Table Compaction: Automatically identify and compact small files to optimize read performance and storage efficiency.
• Adaptive Partition Management: Dynamically recommend or adjust partition strategies based on access patterns.
• Table Health Metrics: Monitor table size, file counts, snapshot growth, and manifest health directly in the Workspace.
• Auto-Maintenance Scheduling: Define AI-driven maintenance schedules based on workload intensity and cost efficiency.
• Integration with AI-Assisted Monitoring: Surface table-level anomalies, performance degradation, or storage bloat alerts.
• Domain Isolation Aware: Each domain manages optimization policies independently, respecting data-mesh boundaries.
• Auto-Tuning of File Sizes & Layouts: AI-driven adjustments to data file sizing and clustering based on read/write workloads.
• Query-Aware Optimization: Analyze query plans to detect inefficient joins or scans, and suggest restructuring.
• Table Version Insights: Visual timeline of changes, optimizations, and AI-triggered events per table.
• AI-Generated Recommendations: Contextual guidance such as “This table’s small file ratio exceeds 70% — a merge is recommended.”
• Integration with Governance & Lineage: Automatically update metadata catalogs and lineage graphs when optimizations alter data structures.

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