Connect, govern, and serve data across silos - without ripping anything out.
Most data fabric tools sell you a vision that requires moving everything you have. Logiciel takes a different approach: virtualization, semantic layer, lineage, and governance across the systems you already run - so you get the value of a data fabric without the migration cost.
If you've lived through a 'consolidate everything into the lake' project, you know:
Teams arriving here typically need:
Data fabric without the migration tax.
Federated query - join across Snowflake, Postgres, S3, Salesforce without ETL. Federated query without ETL eliminates a structural class of integration cost while preserving data residency, which most centralization plans break.
Semantic layer - one definition of revenue, customer, and ARR; reused by BI, apps, and AI. Semantic layer with one definition of revenue, customer, and ARR eliminates the cross-system reconciliation work that consumes 20-30% of analyst time.
Cross-domain lineage and policy - governance that travels with the data. Cross-domain lineage and policy means governance travels with the data, not with the tool - which is why federation works at scale.
Mesh-friendly - domain teams stay autonomous; the fabric provides the connective tissue. Mesh-friendly architecture means domain autonomy and central governance coexist; you don't have to choose between them.
Trading data, risk models, regulatory reporting — sub-second SLAs and audit-ready governance.
Listing data, transaction pipelines, geospatial analytics — multi-source consolidation.
EHR integration, claims pipelines, clinical analytics — HIPAA-aware infrastructure.
Product analytics, customer 360, usage-based billing — embedded and operational data.
Inventory, pricing, order, and customer pipelines — real-time and high-throughput.
IoT, project, and supply-chain data — operational analytics on hybrid stacks.
| Dedicated Pod | Staff Augmentation | Project-Based Delivery |
|---|---|---|
| Embedded data engineering pod aligned to your sprint cadence — typically 3–6 engineers + a US lead. | Senior data engineers, architects, and SMEs slotted into your team to unblock specific work. | Fixed-scope, milestone-driven engagements with clear deliverables and outcomes. |
We map your stack, workloads, team, and constraints in a working session - not an RFP response.
Reference architecture grounded in your reality, with capacity, cost, and migration plans.
Iterative implementation with weekly demos, code reviews, and your team in the loop.
Managed operations or knowledge transfer - your choice. Both with US-aligned coverage.
Continuous tuning of cost, performance, and reliability against measurable SLAs.
Push-down query across cloud and on-prem sources.
Domain-owned metadata, federation-aware search.
Live views across operational and analytical stores.
Centralized metric definitions, decentralized consumption.
Lineage, policy, and quality enforced across systems.
Federated policy with central audit.
Mesh is an organizational pattern (federated domain ownership of data products); fabric is a technical architecture (virtualization, semantic layer, distributed catalog, federated query across heterogeneous sources). They're complementary, not competing - most US enterprises end up with elements of each. Mesh tells you who owns data; fabric tells you how data integrates without being centralized. Logiciel supports both: domain teams can own data products (mesh-style) while consumers query across them via a federated semantic layer (fabric-style) without requiring central ETL. Most customers don't need to choose; they need a platform flexible enough to support whatever their org structure demands.
Federated architecture is ideal for multi-region -data stays in-region; queries are routed and partially executed where the data lives, and only result sets cross boundaries (with classification controls preventing PII from being aggregated cross-border). We have customers running EU/US/APAC fabric configurations with GDPR, US state privacy laws (CCPA, VCDPA, CPA), and APAC regional rules enforced at the platform layer. The semantic layer provides one query surface; the execution layer respects every regional constraint. For US Federal and FedRAMP customers, fully air-gapped fabric deployments are supported.
Denodo is a mature data virtualization platform - strong in regulated, on-prem-heavy enterprises but expensive, slow to deploy, and oriented toward older architecture patterns (heavy stewardship, GUI-driven modeling). Logiciel is fabric-focused with modern engineering ergonomics: Git-native, Terraform-friendly, API-first, cloud-first. For US customers building on Snowflake/Databricks/BigQuery with dbt-style transformation patterns, Logiciel typically delivers fabric capability at 40-60% lower TCO and 3-5x faster time to first federated query. Denodo customers migrating to Logiciel report similar capability with better engineering velocity. We don't recommend Logiciel for customers committed to fully on-prem, mainframe-heavy stacks - Denodo wins there.
A single domain - usually the most painful one - to prove the architecture before federating. Common starting points: customer 360 (integrates 4-6 systems naturally), financial reporting (high pain, well-bounded scope), or post-acquisition data integration (urgent, executive-sponsored). The 90-day pilot establishes the semantic layer for one domain, federates 3-5 source systems, and serves 2-3 high-value consumer workloads (a BI dashboard, an operational app, an AI agent). After pilot, customers typically expand to 2-3 additional domains per quarter, completing fabric rollout in 12-18 months for mid-size enterprises and 18-30 months for Fortune 500 footprints.
No - the entire point of data fabric is to operate where the data lives. Federated query and virtualization let you join across Snowflake, Postgres, S3, Salesforce, and SAP without copying data four ways. Performance is comparable to ETL'd queries for most analytical workloads; for hot paths, we automatically materialize cached views with TTL-based invalidation. Copies happen only when performance demands it, transparently to the analyst. This is particularly valuable for multi-region (data stays in-region for residency), post-acquisition (different cloud per acquired entity), and regulated workloads (PII never leaves its protected zone).
Metric and entity definitions are versioned in Git, code-reviewed, and served via a query API. BI tools (Looker, Tableau, Mode), apps, and AI agents consume the same definitions, so 'revenue' or 'customer ARR' means the same thing everywhere - no more three-way KPI mismatches between Sales Ops, Finance, and Product. The semantic layer is dialect-aware (compiles to Snowflake SQL, BigQuery SQL, Spark SQL, etc.), supports row-level security tied to the consumer's identity, and exposes lineage so analysts can see how a metric is computed end-to-end. This is increasingly the foundation for AI agents that need governed access to enterprise metrics.
Yes - sub-second federated queries support apps and reverse-ETL flows, not just analytics. Common operational patterns: customer service apps querying live customer data across CRM, billing, support, and product analytics in one view; pricing engines pulling SKU, inventory, and competitive data live; fraud workflows joining transaction streams with profile and behavior data. The platform caches hot query patterns and pre-computes materialized views automatically, so consumer SLAs (typically 100-500ms p99) are met without analysts manually tuning. For sub-100ms hard real-time, we recommend specialized in-memory stores; for everything else, fabric works.
Book a 30-minute fabric design session with a Logiciel architect. Bring your messiest cross-system question. We'll show you the federated query that answers it without moving any data.
