Why AI Agents Need a Data Harness, Not Just a Lakehouse
Summary
The talk outlines the necessity of a 'data harness'—a modern data foundation optimized for conversational-pace querying by AI agents. Traditional data pipelines built for minutes-to-hours turnaround fail under this rapid load, leading to errors and wasted tokens. The solution requires combining an open lakehouse architecture (using standards like Apache Iceberg and Apache Arrow) with a robust semantic layer, advanced caching (like Columnar Cloud Cache/C3), and federation capabilities to ensure data is accessible, understandable, and performant for AI use cases.
Key takeaways
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AI Agents Require Subsecond Performance
6:32
Conversational analytics moves at a conversational pace. Legacy systems designed for minutes-to-hours turnaround cannot support this speed; subsecond response time is a critical requirement for agentic workflows.
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Three Pillars of AI-Ready Data Foundation
9:32
Any modern data foundation must be accessible (seeing the whole estate), understandable (having clear, standardized definitions via a semantic layer), and performant (querying data where it lives without moving or cleaning it).
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Open Standards Prevent Vendor Lock-in
24:12
The use of open standards like Apache Iceberg, Apache Arrow, and Apache Polaris ensures that the data platform remains vendor-neutral. This allows users to build performance without sacrificing portability.
Technical details
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Data Lakehouse Architecture
264s
A lakehouse combines the low-cost, scalable storage of a data lake (e.g., S3) with the reliable asset transactions typically found in a data warehouse. This requires three components: 1) Object Storage (the raw data), 2) A Metadata Catalog (like Apache Polaris or Nessie), and 3) A Metadata Format/Table Format (like Apache Iceberg, which provides schema and history management).
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Performance Data Harness Components
1043s
The performance harness relies on: **Semantic Layer:** Consolidating business context into a single layer to prevent AI agents from guessing definitions (e.g., what 'customer' or 'revenue' means). **Federation:** Querying data in place across multiple systems (databases, object storage) without physical centralization. **Caching/C3:** Using techniques like Columnar Cloud Cache (C3) and query plan caching to store frequently accessed data near the compute layer, drastically improving speed.
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Advanced Data Optimization
1205s
To improve performance, techniques include: **Clustering:** Grouping similar values in rows for better read efficiency. **Small File Problem Mitigation:** Compacting many small files into fewer, larger ones. **Variant Shredding:** A feature added to Iceberg/Parquet that allows semi-structured data (like JSON) to be efficiently queried within a cell, enabling predicate pushdown on nested data.
Mentioned resources
- Apache Iceberg
- Apache Arrow
- Apache Polaris
Channel & topics
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