FrostTuneIQ brings intelligence to Apache Iceberg with autonomous data layout optimization. It learns from real workloads, predicts hot partitions, dynamically re-clusters and compacts data, and validates improvements reducing costs and boosting performance without manual tuning.
Autonomous Intelligence + Optimization for Apache Iceberg
FrostTuneIQ is an experimental self-learning optimization layer for modern data lakehouses built on Apache Iceberg.
Instead of relying on static partitions, periodic compaction, and manual tuning, FrostTuneIQ continuously observes query workloads and optimizes data layouts dynamically — making your data lake smarter, faster, and cheaper.
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Workload Telemetry Capture
Continuously collects query patterns filters, joins, scan ranges, latency, and cost metrics. -
Machine Learning Forecasting
Predicts hot partitions, cardinality shifts, and workload spikes before they impact performance. -
Dynamic Optimization Actions
Adjusts partition specs, reclusters data, and triggers compaction automatically. -
Autonomous Validation
Runs canary workloads to measure improvements, with safe rollback if changes backfire.
- Frost → Refers to cold storage and the Iceberg project.
- Tune → Represents performance tuning and optimization.
- IQ → Highlights the intelligence layer powered by machine learning.
Together: FrostTuneIQ = Iceberg + Intelligence + Optimization.
To make Apache Iceberg self-optimizing, bridging the worlds of data engineering and data science, and enabling:
- Reduced compute costs
- Consistent, faster query latency
- Zero manual intervention
Currently in concept and design phase — contributions and discussions are welcome.
Planned implementation includes: Spark / Trino workloads on Iceberg tables with ML-driven layout optimization.
If you’re passionate about lakehouse performance, machine learning, or distributed systems, we’d love to collaborate. Please open issues, propose features, or start discussions.
iceberg, intelligence, optimization, data-engineering, lakehouse, spark, trino, big-data