Fabric Real-Time Intelligence & KQL

Deliver real-time analytics in Microsoft Fabric - stream ingestion, KQL-based querying, operational dashboards, and alerting so you can act on events as they happen.

Real-time data is increasingly required for security operations, service reliability, customer experience, and IoT-style telemetry - yet many organisations still rely on batch refresh cycles that create delayed insight. Microsoft Fabric’s Real-Time Intelligence experience supports streaming ingestion and high-performance analytics using KQL (Kusto Query Language), enabling teams to query event data quickly and build operational insights and alerting on top. Where real-time initiatives fail is usually not the tooling - it’s the lack of an end-to-end pattern: consistent ingestion, schema governance, query standards, retention and cost controls, and operational ownership for dashboards and alerts.

LW IT Solutions delivers Fabric Real-Time Intelligence & KQL as a structured service to design and implement real-time analytics that remain reliable and cost-effective. We help you define event-driven use cases and success criteria, design the ingestion and data model approach for streaming datasets, implement KQL query standards and dashboards, and introduce operational controls for retention, monitoring, and alerting. You receive a working real-time solution (pilot or first use case), plus an operating model and backlog so your organisation can expand to additional streams without losing reliability or governance.

Talk through your requirements and leave with a clear next-step plan.

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Service Overview

Highlights

Event-driven architecture for real-time analytics
KQL-based querying with reusable query standards
Operational dashboards with alerting for immediate response
Documented ingestion, schema, retention, and monitoring practices
Backlog for scaling to additional streams and governance maturity

Business Benefits

Gain actionable insights from event data with low-latency access
Standardise ingestion, schema, and KQL query patterns for consistency
Deliver operational dashboards and alerts aligned to business outcomes
Control costs and data retention with clear operational guidelines
Provide a repeatable model that scales to additional streams and use cases

Typical use cases

Monitoring service performance and uptime with real-time event logs
Security operations and incident detection using streaming telemetry
Customer experience analytics based on live interaction events
IoT device telemetry and operational monitoring
Alerting on business KPIs and threshold breaches in near real-time

Objectives & deliverables

What Success Looks Like

Turn event data into actionable insight with low latency (minutes/seconds rather than days)
Standardise real-time ingestion and query patterns using KQL
Deliver operational dashboards and alerts aligned to business outcomes
Implement retention and cost guardrails for streaming datasets
Create a repeatable pattern that can scale across additional use cases

What You Get

Real-time architecture pack: ingestion, schema, retention, and operational approach (documented)
Implemented pilot use case: streaming ingestion + KQL queries + dashboard/visuals + alerting (as scoped)
KQL query standards and reusable components (where appropriate)
Operational handover pack: monitoring guidance, runbooks, and ownership model recommendations
Backlog for additional streams, dashboards, alert rules, and governance maturity

How It Works

Discovery - identify streaming data sources, use cases, and success criteria
Design - define ingestion pipelines, schema governance, retention policies, and operational model
Implementation - build streaming ingestion, KQL queries, dashboards, and alerting as scoped
Validation - test real-time queries, dashboard accuracy, and alert triggers
Operationalise - document monitoring, runbooks, and ownership responsibilities
Backlog Planning - capture future streams, dashboards, and alert rules for phased expansion

Engagement Options

Pilot Implementation - single use case with end-to-end real-time ingestion, KQL queries, and dashboard
Extended Deployment - multiple streams with reusable KQL components, alerting, and operational guidance
Advisory Session - review existing data flows, KQL patterns, and operational practices to improve reliability

Common Bundles

Customers who use this service often bundle with these services

Fabric Governance, Security & Cost Control
Establish Microsoft Fabric governance with workspace strategy, role based access, auditing, environment separation, and cost controls for predictable operation.

Fabric Data Factory (ETL/ELT) Pipelines
Design and build Microsoft Fabric Data Factory pipelines with repeatable patterns, reliable scheduling, monitoring, and error handling across data sources.

Sentinel Deployment & Integration
Deploy Microsoft Sentinel with structured data onboarding, workspace design, RBAC, and detection content so your SOC operates effectively and predictably.

SOC Use-Case & Detection Engineering
Define SOC detection use cases and engineer Microsoft Sentinel analytics rules mapped to risk, reducing noise and improving incident focus.

Legacy SIEM to Microsoft Sentinel Migration
Migrate legacy SIEM detections, workflows and data into Microsoft Sentinel with phased cutover that maintains monitoring continuity for security operations teams.

Power BI Dashboard Design & Integration
Power BI dashboard design and integration delivering trusted executive and operational reporting through strong data modelling, security and reliable refresh.