The Azure Data Landscape
Azure has dozens of data services. Before we dive into each one, let's get the bird's-eye view — which service does what, and how they connect.
Azure’s data services — the big picture
Azure has a data service for almost every job. Think of it as a toolbox.
Need a screwdriver (relational database)? That’s Azure SQL Database. Need a saw (non-relational storage)? Azure Cosmos DB. Need to move heavy things (data pipelines)? Azure Data Factory. Need to see the finished product (dashboards)? Power BI.
This module is your map of the toolbox. In the next three domains, you’ll learn to use each tool in detail.
The four categories of Azure data services
1. Relational databases (Domain 2)
For structured data with fixed schemas, queried using SQL.
| Service | What It Is | When to Use |
|---|---|---|
| Azure SQL Database | Fully managed SQL Server as a service | Web apps, line-of-business apps, SaaS products |
| Azure SQL Managed Instance | SQL Server with near-100% compatibility, managed by Azure | Migrating on-premises SQL Server to cloud |
| SQL Server on Azure VMs | Full SQL Server running on a virtual machine | When you need full OS and SQL Server control |
| Azure Database for MySQL | Managed MySQL | Apps built on MySQL (WordPress, Laravel) |
| Azure Database for PostgreSQL | Managed PostgreSQL | Apps built on PostgreSQL, GIS workloads |
2. Non-relational storage (Domain 3)
For flexible, unstructured, or semi-structured data.
| Service | What It Is | When to Use |
|---|---|---|
| Azure Blob Storage | Massive object storage for files, images, videos, backups | Unstructured data, media, backups, data lake raw zone |
| Azure Files | Managed file shares (SMB/NFS) | Replacing on-premises file servers, shared config files |
| Azure Table Storage | Simple key-value storage for structured NoSQL data | Configuration data, device info, simple lookups |
| Azure Cosmos DB | Globally distributed, multi-model NoSQL database | High-scale apps, global distribution, flexible schemas |
3. Analytics & data processing (Domain 4)
For ingesting, transforming, and analysing large volumes of data.
| Service | What It Is | When to Use |
|---|---|---|
| Microsoft Fabric | End-to-end analytics SaaS platform (lakehouses, warehouses, pipelines, real-time, Power BI) | Modern analytics — one platform for everything |
| Azure Databricks | Apache Spark-based analytics for big data and ML | Advanced data engineering, data science, large-scale processing |
| Azure Data Factory | Data integration and ETL/ELT pipeline service | Moving data between sources, scheduling data flows |
| Azure Stream Analytics | Real-time stream processing | IoT data, live event processing, real-time dashboards |
4. Visualisation & reporting
| Service | What It Is | When to Use |
|---|---|---|
| Power BI | Business intelligence and data visualisation platform | Dashboards, reports, data models, self-service analytics |
How they connect — Jake’s CloudPulse scenario
Jake’s SaaS startup uses multiple Azure data services together:
- Azure SQL Database stores customer accounts and subscription data (transactional)
- Azure Blob Storage stores customer-uploaded files and backups
- Azure Cosmos DB stores real-time application session data (flexible schema, global)
- Azure Data Factory copies daily snapshots from SQL Database and Azure Cosmos DB into a Microsoft Fabric lakehouse
- Microsoft Fabric hosts the data warehouse where Jake’s analytics team runs queries
- Power BI dashboards show the CTO monthly revenue trends and customer growth
One platform to rule them all: Microsoft Fabric
Microsoft Fabric is the newest addition to Azure’s data landscape. It’s a SaaS platform that combines:
- Data Factory capabilities (pipelines and data movement)
- Data engineering (Spark-based processing)
- Data warehousing (SQL-based warehouse)
- Real-time analytics (streaming and event processing)
- Power BI (visualisation and reporting)
All built on a single storage layer called OneLake. Think of Fabric as a “one-stop shop” for analytics — you’ll explore it in detail in Domain 4.
Exam tip: service-to-scenario matching
The exam loves asking “which service should you use?” Here’s a quick decision tree:
- Need structured tables with SQL? → Azure SQL Database (or MySQL/PostgreSQL)
- Need flexible JSON documents at global scale? → Azure Cosmos DB
- Need to store files, images, or backups? → Azure Blob Storage
- Need to build data pipelines? → Azure Data Factory (or Fabric pipelines)
- Need to run analytics on large datasets? → Microsoft Fabric or Azure Databricks
- Need dashboards and reports? → Power BI
- Need to process streaming data in real time? → Stream Analytics or Fabric Real-Time Intelligence
Flashcards
Knowledge check
Aisha's food ordering app needs a database for order processing (fixed schema: order ID, student, items, total, timestamp) with multiple users placing orders simultaneously. Which Azure service should she choose?
Tom's logistics company wants to analyse 6 months of delivery data to find the most efficient routes. The data comes from GPS trackers, dispatch systems, and customer feedback forms. Which combination of services makes MOST sense?
You’ve completed Domain 1! 🎉 You now understand the core data concepts — data types, file formats, databases, workloads, roles, and Azure services.
Next up: Relational Data: Tables, Keys & Relationships — let’s dive deeper into how relational databases actually work.