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Welcome to the Azure Data Fundamentals course. In this lesson, we’ll explore how to store and manage data in Azure. This content aligns with the DP-900 certification exam objectives. I’m Peter Vogel, and I’ll guide you through:
The image is a slide titled "Objectives" with three points: "Variety of organizational data," "Data Storage Tools," and "Roles and Responsibilities of Data Managers."
  • The diversity of organizational data
  • Azure’s data storage tools
  • Key roles and responsibilities in data management
Organizations handle everything from images and documents to structured records and semi-structured logs. Choosing the right Azure storage solution is critical for performance, scalability, and global access. We’ll also cover the primary roles responsible for securing and analyzing that data.

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Data Types and Storage Needs

Data formats and usage patterns drive storage requirements: When storing files—such as pictures or PDFs—you must download each file in its entirety; partial downloads are not practical. Databases, by contrast, read and update only the necessary portions of a record. Use cases vary:
  • Analyze historical data for trends and forecasting.
  • Process hundreds of thousands of transactions per second with low latency.
  • Share updates globally so Berlin’s changes appear instantly in Singapore.
The image is an introduction slide showing three categories: Files, Structured Data, and Semi-structured Data, each represented by an icon.
The image illustrates three data storage options: analyzing data, scaling to support millions of transactions, and sharing data around the world, each represented by an icon.

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Azure Data Storage Solutions

Azure provides a range of storage services for different data scenarios:
The image is a diagram showing Azure Storage accounts with icons for "Files" and "Blobs." It is labeled with "01 Files" on the left.

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Choosing Between Azure Files and Azure Blobs

  • Azure Files: Best for lift-and-shift applications using SMB or NFS protocols.
  • Azure Blobs: Optimized for large-scale object storage with REST APIs and tiered access.
Consider lifecycle management policies to move blobs between hot, cool, and archive tiers to optimize cost.

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Analytics Workflow

For analytics scenarios, Azure follows an Extract–Transform–Load (ETL) pattern:
  1. Extract data from files, databases, and NoSQL stores
  2. Transform into a common schema
  3. Load into a data warehouse for high-performance querying
Visualization and BI tools then convert raw data into actionable insights.
Azure Synapse Analytics integrates data warehousing, big data, and data integration in a single service—ideal for end-to-end analytics.

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Roles and Responsibilities

Data security, privacy, and expertise drive role assignments:
The image is a diagram showing the need to restrict data for security, privacy, and expertise, alongside roles and responsibilities including users, administrators, analysts, auditors, and engineers.
Grant the principle of least privilege. Always assign minimal permissions required for each role to reduce security risks.
In the next sections, we’ll dive deeper into file-based storage with Azure Files and Blob Storage. Stay tuned!

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