Iceberg LLM Grounding and RAG for Structured Data

LLM Grounding with Apache Iceberg

LLM grounding refers to the practice of anchoring large language model (LLM) responses in verified, authoritative data — preventing hallucination by ensuring the model’s outputs are based on actual facts retrieved from trusted sources. For structured data use cases (business analytics, operational reporting, financial metrics), Apache Iceberg is the ideal grounding data store because it provides:

• Verified data: Governed, quality-controlled tables with known provenance.
• Current data: Real-time or near-real-time updates via streaming ingestion.
• Versioned data: Specific snapshots ensure reproducible, auditable AI responses.
• Governed access: REST Catalog RBAC ensures the LLM only accesses authorized data.

The Structured Data RAG Problem

Traditional RAG (Retrieval-Augmented Generation) is designed for unstructured data: retrieve relevant text chunks from a vector store, inject them into the LLM’s context window. But analytics use cases need structured data retrieval:

• “What was our revenue last quarter?” → not a text chunk problem, a SQL query problem.
• ”Which customers are at risk of churn?” → not a document retrieval problem, a table scan problem.
• ”How does this month’s performance compare to last year?” → a temporal query problem.

Iceberg provides the infrastructure for structured data RAG: the LLM generates SQL or uses MCP tools to query Iceberg tables, retrieves precise numerical results, and incorporates those results into its response.

Structured Data RAG Patterns

Pattern 1: Text-to-SQL + Iceberg (NL2SQL)

The most common pattern: an LLM generates SQL, a query engine executes it against Iceberg, results ground the final response.

from anthropic import Anthropic
import duckdb
from pyiceberg.catalog import load_catalog

client = Anthropic()
catalog = load_catalog("my_catalog", **{...})

def query_iceberg(sql: str) -> str:
    """Execute SQL against Iceberg and return results."""
    table = catalog.load_table("analytics.orders")
    arrow_table = table.scan().to_arrow()
    conn = duckdb.connect()
    conn.register("orders", arrow_table)
    result = conn.execute(sql).fetchdf()
    return result.to_markdown()

# System prompt includes table schema as context
schema_context = str(catalog.load_table("analytics.orders").schema())

response = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=2048,
    system=f"""You are an analytics assistant. Answer questions using SQL queries
against the 'orders' Iceberg table with this schema:
{schema_context}

When you need data, write a SQL SELECT query and I will execute it.""",
    messages=[{
        "role": "user",
        "content": "What were our top 5 products by revenue this month?"
    }]
)

# Extract SQL from response, execute against Iceberg, return grounded answer
sql = extract_sql(response.content[0].text)
data = query_iceberg(sql)
# Feed data back to LLM for final natural language response

Pattern 2: MCP Tool-Calling (Agentic)

More powerful: the LLM calls MCP tools autonomously to discover tables, inspect schemas, and run queries without the developer pre-specifying the schema.

# MCP server exposes Iceberg tools
# LLM calls: list_tables → describe_table → query_iceberg
# See /iceberg/iceberg-mcp/ for full MCP implementation

Pattern 3: Semantic Layer Grounding (Dremio)

The most robust pattern: use Dremio’s AI Semantic Layer to pre-define the data context, then let the AI Agent generate queries within that governed semantic context.

User question → Dremio AI Agent
  → Discovers semantic layer (Virtual Datasets, metrics, descriptions)
  → Generates SQL grounded in semantic context
  → Executes against Iceberg via Dremio Intelligent Query Engine
  → Returns validated numerical result
  → Synthesizes natural language answer

This pattern reduces prompt-engineering burden: the semantic layer replaces the need to inject raw table schemas into every LLM prompt.

Snapshot Versioning for Auditable AI Responses

When an LLM generates an analytics answer, record the Iceberg snapshot used:

# Track which snapshot grounded the AI response
snapshot_id = table.current_snapshot().snapshot_id

response = {
    "answer": llm_generated_answer,
    "grounding_data": query_results,
    "snapshot_id": snapshot_id,
    "table": "analytics.orders",
    "generated_at": datetime.utcnow().isoformat()
}

# Later: reproduce the exact data that grounded the answer
reproduce_data = table.scan(snapshot_id=snapshot_id).to_arrow()

This enables:

• Audit trails: “What data did the AI use to answer this question on 2026-05-14?”
• Reproducibility: Reproduce the exact query results that informed a decision.
• Compliance: Demonstrate to regulators what data sources informed AI-driven recommendations.

Hallucination Reduction Metrics

The effectiveness of Iceberg grounding can be measured:

• Without grounding: LLM invents revenue figures, customer counts, dates.
• With SQL grounding: LLM generates SQL → exact numbers from Iceberg → no fabrication.
• With semantic layer: LLM uses pre-validated metric definitions → business-correct answers.

For critical business analytics (financial reporting, operational KPIs), structured data grounding via Iceberg is mandatory — ungrounded LLM responses to quantitative questions are fundamentally unreliable.

Key Principle: Iceberg as the Ground Truth

In any LLM-powered analytics system:

• The Iceberg table is the authority.
• The LLM is the interface that makes the authority accessible in natural language.
• The query engine (Dremio, Trino, Spark, DuckDB) is the bridge between the LLM’s SQL and the Iceberg data.

The LLM should never generate numbers — it should retrieve them from Iceberg. This is the fundamental design principle of grounded AI analytics.