Unified Analytics & AI Platform Partnership
Turning Enterprise Data into
Real‑Time, Governed Intelligence
Logesys is an official Microsoft Fabric implementation partner, helping enterprises modernise analytics, consolidate fragmented data estates, and prepare governed, AI‑ready data foundations on a single SaaS platform.
The Platform
What Is Microsoft Fabric?
Microsoft Fabric is an end-to-end, AI-powered analytics platform that unifies data integration, data engineering, data warehousing, real-time analytics, data science, and business intelligence into a single SaaS experience — all built on OneLake, Microsoft’s unified data lake for the enterprise.
Rather than operating as separate services stitched together through custom integration, Fabric provides one shared storage layer, one security and governance model, and one operating plane for analytics and AI. Every workload — Spark, SQL, KQL, and Power BI — operates on the same physical copy of data, dramatically reducing complexity while improving trust, performance, and cost efficiency.
The Core Pillars of Microsoft Fabric
OneLake — One Logical Data Lake for the Enterprise
OneLake is the tenant-wide, unified data lake at the heart of Fabric. Every workload reads and writes to OneLake directly — no duplicated storage, no synchronisation pipelines, no format or engine lock-in. It stores data in open Delta Lake format and can expose Delta tables through Apache Iceberg-compatible interfaces via OneLake Table APIs.
Direct Lake — Warehouse-Grade BI Without Import Pipelines
Direct Lake allows Power BI semantic models to read Delta tables directly from OneLake, delivering near-import query performance with near-real-time freshness. No long refresh windows, no duplicated VertiPaq data copies, and no full import refresh cycles. This is the recommended serving pattern for all Gold layer analytics in Fabric.
Real‑Time Intelligence — Streaming & Event Analytics Built In
Fabric includes native support for event-driven and streaming scenarios through Eventstream and KQL databases (Eventhouse). IoT telemetry, operational events, application logs, and high-volume transactional streams are analysed in seconds and persisted back into OneLake for downstream analytics and AI — without a separate streaming platform.
Fabric IQ & Data Agents — Intelligence Built In
Fabric IQ, announced at Microsoft Ignite 2025, introduces a semantic intelligence layer that powers Data Agents and Copilot experiences across Fabric. It integrates with Azure AI Foundry and Copilot Studio to enable agentic AI grounded in OneLake data.
Mirroring — Operational Data into OneLake Without ETL
Fabric Mirroring is now generally available for SQL Server (2016–2025), PostgreSQL, Azure Cosmos DB, and Snowflake (including Iceberg tables). Operational data replicates continuously into OneLake as Delta tables — with changes available in as little as 15 seconds — with no separate replication service or licensing costs. SAP mirroring via SAP Datasphere is in preview.
Microsoft Purview — Governance Across Every Engine
Fabric integrates natively with Microsoft Purview for end-to-end lineage, sensitivity labels, asset discovery, and domain-aligned ownership. OneLake Security (Public Preview 2025) enforces access controls directly on tables in OneLake — so security travels with the data regardless of which engine queries it.
2024 – 2025
Platform Innovations
Microsoft ships Fabric updates every month. The period from 2024 to 2025 delivered some of the most significant platform advances since general availability — spanning agentic AI, zero‑ETL data integration, unified security, open table format support, and a new intelligence layer that sits above the entire platform.
Mirroring — SQL Server, PostgreSQL, Cosmos DB
Native mirroring for SQL Server 2016–2022 and SQL Server 2025 is now generally available, alongside PostgreSQL and Cosmos DB. Operational data replicates continuously into OneLake as Delta tables using CDC technology — no ETL pipelines, no cost for mirroring, and an automatic SQL analytics endpoint on every mirrored database.
Fabric SQL Database & Cosmos DB in Fabric
The Fabric SQL Database is now generally available — a fully managed, serverless SQL database within Fabric, optimized for transactional‑analytical workloads. Cosmos DB mirroring is also GA, enabling SQL queries over JSON operational data with Power BI and AI workloads without separate services.
OneLake Security — Unified Access Control
OneLake Security introduces data‑layer access control in Microsoft Fabric. In Preview, role‑based permissions are defined on Lakehouses, Warehouses, and Mirrored Databases. At GA, Spark, SQL, KQL, and Power BI will enforce access rules from a single definition, simplifying governance and compliance.
Apache Iceberg Support — Automatic Format Translation
OneLake now automatically serves Delta Lake tables as Apache Iceberg to any compatible engine — Trino, Snowflake, Dremio, Spark — without copying data or reconfiguring pipelines. The OneLake Table APIs (Preview) also provides programmatic Iceberg REST Catalog access for integration with external tooling.
Fabric IQ — From Data Platform to Intelligence Platform
Announced at Microsoft Ignite 2025, Fabric IQ is a semantic intelligence layer that turns raw data structures into business ontologies — entities, relationships, properties, rules, and actions. It powers Data Agents, integrates with Foundry IQ and Work IQ, and will be accessible via an MCP server, enabling agents to discover and act on business context.
Autoscale Billing for Spark — Pay-As-You-Go Compute
Spark Autoscale Billing enables a pay‑as‑you‑go model for Data Engineering workloads — offloading Spark jobs to a serverless billing mode independent of the primary Fabric capacity. Capacity admins set a CU limit to prevent shared workloads from consuming reserved capacity during peak periods.
Materialized Lake Views — No-Code Medallion Pipelines
Materialized lake views are now generally available, enabling always‑up‑to‑date medallion pipelines in Spark SQL and PySpark without manual orchestration. Defined once, they refresh automatically as data arrives — dramatically reducing pipeline maintenance overhead for Bronze‑to‑Silver‑to‑Gold transformations.
Fabric MCP — AI Agents Connected to Fabric
Fabric local MCP is generally available as an open‑source server connecting AI coding assistants (GitHub Copilot) directly to Fabric. Fabric remote MCP is in preview — a secure, cloud‑hosted execution engine enabling AI agents and automation tools to perform authenticated actions against Fabric items and data programmatically.
Fragmented stacks create
predictable enterprise failure
Most enterprises today run fragmented analytics stacks that evolved one tool at a time — separate ETL platforms, multiple data lakes and warehouses, standalone streaming systems, disconnected BI semantic layers, and isolated AI environments. Microsoft Fabric was built to eliminate this fragmentation at the architectural level — not just at the tooling level.
Separate ETL, data lake, warehouse, streaming, BI semantic layer, and AI environments — each maintained independently by different teams.
Multi‑platform coordination is required for every new use case, dramatically increasing time-to-value and operational overhead across the organisation.
The environment becomes unnecessarily expensive and complex.
Multi‑platform coordination is required for every new use case, dramatically increasing time-to-value.
Each platform stores its own copy of data with its own access model, creating multiple sources of truth and duplicated storage costs.
Data is siloed across systems with no single authoritative version of the truth available to any team.
Data inconsistency is endemic. Governance is applied differently in each system.
Lineage, audit readiness, and access control become extremely difficult to prove, making lineage and compliance a manual, error-prone process.
Governance is implemented per tool — Spark has its own model, SQL has its own, KQL has its own, and Power BI has its own. None of them align.
Each tool enforces access independently with no unified policy definition or consistent enforcement across the stack.
Security gaps emerge between tools. Compliance exercises require manual evidence gathering across systems.
Teams cannot confidently share data with one another, and audit preparation becomes a significant, recurring manual burden.
AI and analytics initiatives operate on different copies of data, requiring continuous synchronisation and creating risk that models train on stale or incorrect inputs.
The AI estate and the analytics estate are structurally disconnected, requiring constant bridging effort from engineering teams.
AI projects are slow, expensive, and difficult to trust.
The gap between the analytics estate and the AI estate prevents production‑grade deployment at scale.
Real‑world outcomes from Microsoft Fabric in production
Organizations that have deployed Microsoft Fabric in production and achieved strong results demonstrate what Logesys helps clients accomplish—through deliberate platform architecture, governance-first deployment, and Fabric-aligned delivery.
in Microsoft Fabric
The Medallion Architecture is Microsoft's recommended design pattern for organising data in Fabric — and the natural fit for OneLake as the foundation. It structures the lakehouse into three progressive layers of data quality, each with a distinct owner, SLA, and purpose.
All three layers live in OneLake. No copying between platforms, no switching engines, no synchronisation overhead. Spark, SQL, KQL, Power BI, and Data Agents all operate on the same underlying Delta tables — governed by a single security model.
on Microsoft Fabric
We bring end‑to‑end delivery capability across the full Fabric platform — from initial architecture blueprint to production deployment and ongoing managed optimisation. Every engagement begins with a written architecture design reviewed before implementation starts. No surprises mid‑delivery.
Microsoft Fabric Partner
on Microsoft Fabric?
Whether you are consolidating a fragmented analytics stack, modernizing Power BI at scale, or laying a governed foundation for AI — Logesys helps you move from platform capability to production value faster.