Four Iceberg Tools, Three Agent Frameworks: What Ports, and What Doesn't

7.9 relevance

Benchmarking Iceberg tools across agent frameworks is technically deep, novel, and highly relevant to data engineering and agent orchestration.

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Why It Matters

For a solutions architect evaluating multi-cloud agent orchestration, this empirically quantifies that framework choice (ADK, Strands, Agent Framework) impacts latency more than agent logic, and that storage plumbing (GCS, S3, ADLS) is the real portability bottleneck, not the Iceberg catalog API.

Key Takeaways

Standardize on PyIceberg scan logic across frameworks but budget 3–4x runtime variance between ADK and Agent Framework; test storage wiring separately from catalog queries.

Four Iceberg Tools, Three Agent Frameworks: What Ports, and What Doesn't

Summary

A data agent built with Google ADK, AWS Strands, and Microsoft Agent Framework runs identically against five Apache Iceberg catalogs, but speed varies 3.72x at the median (3.33s vs 12.36s) and per-cloud storage wiring requires distinct configurations for OneLake, Glue, or S3 Tables, while the framework itself adds a smaller cost — e.g., Strands on Gemini was 1.52x slower than ADK. All three agents produced the same answers because PyIceberg scan filters and snapshot references operate consistently across catalogs.

Author

xbill