What Is a Data Mart?

What Is a Data Mart?

A data mart is a focused, subject-oriented slice of an enterprise data warehouse, scoped to one team, function, or domain — finance, marketing, supply chain, customer support, or in 2026 increasingly AI evaluation. It exists because giving every analyst the entire warehouse is slow, expensive, and a governance nightmare. Data marts trade breadth for performance, well-defined schemas, and team-specific quality rules. FutureAGI does not build a generic warehouse, but its versioned Dataset, AnnotationQueue, and trace stores effectively form an AI-evaluation data mart for ML, product, compliance, and SRE teams.

Why data marts matter in production LLM and agent systems

When AI teams treat a generic data lake as their working store, three failure modes follow. First, queries become slow and unreproducible — the same SQL returns different results week to week as upstream tables churn. Second, schemas drift across consumers — ML calls a column “score,” product calls it “rating,” compliance calls it “outcome,” and the dashboards disagree. Third, governance breaks — sensitive raw data is everywhere, retention rules are inconsistent, and an audit becomes a forensic exercise.

The pain spans roles. ML engineers want eval inputs that are versioned and reproducible. Product teams want metrics they can trust release-to-release. Compliance teams want evidence with retention rules they can defend. SREs want a queryable trace store they can join to incident timelines. A generic data warehouse can technically serve all of them, but only after each team builds their own mart.

In 2026 LLM stacks, the AI mart needs a specific shape: prompts, retrieved context, model routes, evaluator scores, guardrail decisions, agent trajectories, and audit fields, all version-tracked and joinable. Useful symptoms of mart-shaped problems: eval scores that don’t reproduce, dashboards where the same metric reads different numbers depending on which table you open, and audit responses that take days because the data is everywhere and nowhere.

How FutureAGI handles the AI-evaluation mart

FutureAGI’s approach is honest: we don’t replace your data warehouse, but we give you a focused AI-evaluation store that behaves like a domain mart for ML, product, compliance, and SRE teams. The fi.datasets.Dataset API stores rows with content hash, version, source id, ingestion timestamp, reviewer state, evaluator scores, and tags. Each row is reproducible — re-running an evaluator against version=7 produces the same scoring inputs the first run did.

The trace store, populated by the langchain and openai-agents traceAI integrations, captures span-level data with attributes including agent.trajectory.step and llm.token_count.prompt. The fi.queues.AnnotationQueue API exports labels and rationales to the dataset store, so reviewer decisions become first-class artifacts. Together these form the AI-evaluation mart: focused on AI reliability, governed with retention rules, joinable to upstream warehouse keys (request id, user id, tenant id) without leaking sensitive raw data.

A practical workflow: a release manager runs a regression eval against Dataset version 12 before promoting a prompt change. The mart returns scoring inputs, evaluator outputs, and trace evidence in one query. Compliance pulls the same artifacts under audit. ML uses them to retrain a judge. Unlike rebuilding everything inside Snowflake or BigQuery, FutureAGI’s surfaces give the AI team exactly what AI evaluation needs without forcing the rest of the warehouse to follow. The engineer’s next move is concrete: bump the dataset version, freeze the gate, run the regression, and ship.

How to measure or detect data mart problems

A working AI-evaluation mart shows up as several signals:

• Evaluator reproducibility — share of Groundedness and ContextRelevance runs that produce identical scoring inputs against the same dataset version.
• Schema-stability index — number of breaking schema changes per quarter; a healthy mart approaches zero.
• Cross-team query coverage — share of compliance, ML, product, and SRE queries served by the mart without raw-warehouse access.
• Audit lookup time — time from “show me eval evidence for release X” to a queryable answer.
• Retention-policy compliance — share of rows respecting their declared retention window.

from fi.datasets import Dataset
from fi.evals import ContextRelevance, Groundedness

ds = Dataset(name="rag-eval-q3", version=12)
ds.add_evaluation(Groundedness())
ds.add_evaluation(ContextRelevance())
# Re-running against version=12 next quarter must reproduce the same scoring inputs.

Common mistakes

• Treating the mart as a copy of the warehouse. A mart is curated; if every column of the warehouse leaks in, governance and performance both fail.
• Skipping versioning. Without dataset versions, eval runs are not reproducible and audit defense crumbles.
• Mixing raw PII into the mart. Use redaction, hashing, or join keys; keep raw sensitive data in the warehouse with stricter controls.
• Letting schemas drift. A column renamed across versions is a regression; bump the version and migrate intentionally.
• No cross-team contract. Compliance, ML, and product all read from the mart — agree on field names and meanings, then test for drift.