🔒 Guided

Pre-launch preview. Authorised access only.

Incorrect code

Guided by A Guide to Cloud
Explore AB-900 AI-901
Guided DP-900 Domain 4
Domain 4 — Module 5 of 8 63%
24 of 27 overall

DP-900 Study Guide

Domain 1: Core Data Concepts

  • Your First Look at Data Free
  • Data File Formats: CSV, JSON, Parquet & More Free
  • Databases: Relational vs Non-Relational Free
  • Transactional Workloads: Keeping Data Consistent Free
  • Analytical Workloads: Finding the Insights Free
  • Data Roles: DBA, Engineer & Analyst Free
  • The Azure Data Landscape Free

Domain 2: Relational Data on Azure

  • Relational Data: Tables, Keys & Relationships
  • Normalization: Why Duplicate Data is Bad
  • SQL Basics: SELECT, INSERT, UPDATE, DELETE
  • Database Objects: Views, Indexes & More
  • Azure SQL: Your Database in the Cloud
  • Open-Source Databases on Azure
  • Choosing the Right Azure Database

Domain 3: Non-Relational Data on Azure

  • Azure Blob Storage: Files in the Cloud
  • Azure Files & Table Storage
  • Azure Cosmos DB: The Global Database
  • Cosmos DB APIs: SQL, MongoDB & More
  • Choosing Non-Relational Storage

Domain 4: Analytics on Azure

  • Data Ingestion & Processing
  • Analytical Data Stores: Data Lakes, Warehouses & Lakehouses
  • Microsoft Fabric & Azure Databricks
  • Batch vs Streaming: Two Speeds of Data
  • Real-Time Analytics on Azure
  • Power BI: See Your Data
  • Data Models in Power BI
  • Choosing the Right Visualization

DP-900 Study Guide

Domain 1: Core Data Concepts

  • Your First Look at Data Free
  • Data File Formats: CSV, JSON, Parquet & More Free
  • Databases: Relational vs Non-Relational Free
  • Transactional Workloads: Keeping Data Consistent Free
  • Analytical Workloads: Finding the Insights Free
  • Data Roles: DBA, Engineer & Analyst Free
  • The Azure Data Landscape Free

Domain 2: Relational Data on Azure

  • Relational Data: Tables, Keys & Relationships
  • Normalization: Why Duplicate Data is Bad
  • SQL Basics: SELECT, INSERT, UPDATE, DELETE
  • Database Objects: Views, Indexes & More
  • Azure SQL: Your Database in the Cloud
  • Open-Source Databases on Azure
  • Choosing the Right Azure Database

Domain 3: Non-Relational Data on Azure

  • Azure Blob Storage: Files in the Cloud
  • Azure Files & Table Storage
  • Azure Cosmos DB: The Global Database
  • Cosmos DB APIs: SQL, MongoDB & More
  • Choosing Non-Relational Storage

Domain 4: Analytics on Azure

  • Data Ingestion & Processing
  • Analytical Data Stores: Data Lakes, Warehouses & Lakehouses
  • Microsoft Fabric & Azure Databricks
  • Batch vs Streaming: Two Speeds of Data
  • Real-Time Analytics on Azure
  • Power BI: See Your Data
  • Data Models in Power BI
  • Choosing the Right Visualization
Domain 4: Analytics on Azure Premium ⏱ ~10 min read

Real-Time Analytics on Azure

When data can't wait, Azure provides services for streaming, processing, and analysing data in real time. Meet Stream Analytics, Event Hubs, and Fabric Real-Time Intelligence.

Real-time services on Azure

☕ Simple explanation

Real-time analytics is like a control room — screens showing live data, alarms going off when something’s wrong.

Azure provides three key services for this: Event Hubs catches the incoming data (like a massive funnel), Stream Analytics processes it in motion (filtering, aggregating, alerting), and Fabric Real-Time Intelligence does the same inside the Fabric platform.

Azure’s real-time analytics stack consists of event ingestion services (Azure Event Hubs, Azure IoT Hub) that capture high-throughput data streams, and stream processing engines (Azure Stream Analytics, Fabric Real-Time Intelligence) that query and transform data in motion. These services enable scenarios like live dashboards, real-time alerting, and IoT monitoring.

The real-time pipeline

Data Sources → Event Ingestion → Stream Processing → Output
(IoT, apps)    (Event Hubs)      (Stream Analytics)   (Dashboard, database, alert)

Azure Event Hubs

The front door for streaming data. Event Hubs is a big data streaming platform that can ingest millions of events per second.

Key features:

  • Capture millions of events per second
  • Partitioned for parallel processing
  • Retain events for replay (1-7 days standard, up to 90 days with Premium/Dedicated)
  • Works with Kafka protocol (existing Kafka apps can connect)

Tom’s example: GPS updates from 200 trucks flow into Event Hubs → 200 events every 10 seconds = 1,200 events/minute.

Azure Stream Analytics

A real-time query engine that processes streaming data using SQL-like queries. It reads from Event Hubs (or IoT Hub), applies transformations, and writes results to outputs.

-- Alert when a delivery is more than 30 minutes late
SELECT DriverID, DeliveryID, Delay
FROM GPSStream
WHERE Delay > 30

Key features:

  • SQL-like query language (easy for analysts)
  • Built-in temporal functions (windows, time-based aggregation)
  • Multiple outputs (Power BI, Blob Storage, Cosmos DB, SQL Database)
  • No infrastructure to manage (serverless)

Fabric Real-Time Intelligence

Microsoft Fabric’s built-in real-time analytics capability. It includes:

  • Eventstreams — capture and route streaming data within Fabric
  • KQL Database — a database optimised for real-time queries using Kusto Query Language
  • Real-Time dashboards — live visualisations connected to streaming data

When to use Fabric Real-Time vs Stream Analytics:

  • If you’re already in Fabric → use Fabric Real-Time Intelligence
  • If you need a standalone streaming service → use Stream Analytics
Azure real-time analytics services
FeatureEvent HubsStream AnalyticsFabric Real-Time Intelligence
RoleIngest streaming dataProcess streaming dataEnd-to-end real-time analytics in Fabric
What it doesCatches millions of events/secSQL-like queries on data in motionIngest, query, and visualise in one platform
Query languageN/A (ingestion only)SQL-like (ASA SQL)KQL (Kusto Query Language)
Best forHigh-volume event captureReal-time filtering and alertingReal-time analytics within Fabric
ℹ️ Azure IoT Hub vs Event Hubs

Both ingest streaming data, but for different scenarios:

  • Event Hubs: General-purpose event streaming (application logs, clickstreams, telemetry)
  • IoT Hub: Purpose-built for IoT devices — adds device management, per-device authentication, and cloud-to-device messaging

For DP-900, know that IoT Hub exists for IoT scenarios and Event Hubs is the general-purpose option.

💡 Exam tip: real-time service selection
  • “Ingest millions of events per second” → Event Hubs
  • “Process streaming data with SQL-like queries” → Stream Analytics
  • “Real-time analytics inside Microsoft Fabric” → Fabric Real-Time Intelligence
  • “IoT device management + telemetry ingestion” → IoT Hub
  • “Real-time Power BI dashboard” → Stream Analytics output to Power BI, or Fabric real-time dashboard

Flashcards

Question

What is Azure Event Hubs?

Click or press Enter to reveal answer

Answer

A big data streaming platform for ingesting millions of events per second. It's the front door for streaming data — capturing events from applications, IoT devices, and services before they're processed.

Click to flip back
Question

What is Azure Stream Analytics?

Click or press Enter to reveal answer

Answer

A real-time query engine that processes streaming data using SQL-like queries. It reads from Event Hubs or IoT Hub, applies transformations and filters, and sends results to outputs like Power BI or databases.

Click to flip back
Question

What is Fabric Real-Time Intelligence?

Click or press Enter to reveal answer

Answer

Microsoft Fabric's built-in real-time analytics service. It includes Eventstreams (data capture), KQL Database (real-time queries), and real-time dashboards — all within the Fabric platform.

Click to flip back

Knowledge check

Knowledge Check

Pacific Freight needs to process GPS events from 200 trucks in real time and alert dispatchers when deliveries are more than 30 minutes late. Which Azure service processes the streaming data?
Knowledge Check

A smart factory generates 5 million sensor readings per second. Which service should capture this high-volume data stream before processing?

🎬 Video coming soon

Next up: Power BI: See Your Data — the tool that turns all this data into visual insights.

← Previous

Batch vs Streaming: Two Speeds of Data

Next →

Power BI: See Your Data

Guided

I learn, I simplify, I share.

A Guide to Cloud YouTube Feedback

© 2026 Sutheesh. All rights reserved.

Guided is an independent study resource and is not affiliated with, endorsed by, or officially connected to Microsoft. Microsoft, Azure, and related trademarks are property of Microsoft Corporation. Always verify information against Microsoft Learn.