Concept card

BARC — The Data Fabric Survey

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is BARC — The Data Fabric Survey?

Data fabric is a technology pattern (active metadata, virtualisation, lineage); data mesh is an organisational model (domain-owned data products) — conflating them misdiagnoses which problem a client actually has.

Data fabric is an architectural pattern, not a product you can buy and switch on — a distinction that gets lost constantly in vendor marketing. It describes a design principle: that data integration, access, and governance across an enterprise should be driven by metadata, specifically active metadata, rather than by hand-coded point-to-point pipelines connecting every source system to every consuming application. The promise is a unified, queryable, governed view of enterprise data regardless of where it physically sits — on-premise ERP, cloud warehouses, SaaS applications, data lakes, or operational databases — without requiring every byte to be copied into one place first.

The three mechanisms

A working data fabric rests on three capabilities working together. Active metadata management goes beyond a passive catalog that simply records where data lives; it tracks relationships between datasets, observed usage patterns, schema-change history, and computed quality scores, and it uses that intelligence to recommend joins, flag drift, and automate policy enforcement rather than requiring a human to notice a problem. Intelligent data virtualization lets a consumer query across genuinely heterogeneous sources through a semantic layer without first physically moving all of that data into a central store — the query is federated out to where the data lives and the results are assembled on the fly. Automated lineage tracks the provenance of every data point from its origin through every transformation to its point of consumption, which is what makes impact analysis possible when a source schema changes upstream, and is frequently a regulatory requirement in its own right.

Why it matters

Active metadata goes beyond a passive catalog — it tracks relationships, drift, and quality scores to recommend joins and automate policy enforcement.
A data fabric can be implemented by one centralised engineering team; a data mesh requires distributing ownership across business domains like Finance and Supply Chain.
Lineage enables impact analysis when a source schema changes and supports regulatory data-tracing requirements.

Key points

BARC user-experience survey for the data-fabric category.
Covers data integration + metadata + catalog + quality + governance + orchestration.
Vendor field: Informatica, Talend / Qlik Talend, Microsoft Fabric, Denodo, etc. (BDC competes here).
Newer survey than BI / Planning — category itself is recent.
Rankings paywalled.
BARC — The Data Fabric Survey is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.
Define owner, metric, threshold, support path, and rollback before scaling.

Terms used on this page

Data fabric: Integrated data-management architecture combining ingestion, metadata, catalog, quality, governance and active orchestration under unified semantics — Gartner-popularised term.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.
Evidence grade: A label that separates verified fact, directional signal, modeled assumption, and field observation.
Adoption metric: The measurable behavior proving that the concept changed actual work after go-live.
Reusable IP: An artifact, checklist, or model that can be reused across clients without copying client-specific data.
Decision owner: The accountable person who accepts the trade-off and funds the next action.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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