This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub.
This topic falls under these sections:
Describe considerations for working with non-relational data on Azure (15–20%)
--> Describe capabilities of Azure storage
--> Describe Azure Table storage
Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.
Azure Table Storage is a scalable, low-cost storage solution designed to store large amounts of structured, non-relational data.
For the DP-900 exam, you should understand what Table Storage is, how it is structured, and when to use it compared to other Azure storage options.
What Is Azure Table Storage?
Azure Table Storage is a NoSQL key-value store that:
- Stores data in tables (but not relational tables)
- Does not enforce a fixed schema
- Is optimized for fast access using keys
✔ Despite the name “table,” it is not a relational database.
Key Characteristics
1. Schema-less Design
- Each record (entity) can have different properties
- No fixed columns required across all records
✔ Enables flexibility for evolving data models.
2. Key-Based Access
Each entity is uniquely identified by:
- Partition Key → groups related data
- Row Key → uniquely identifies an entity within a partition
✔ These keys are critical for performance and query efficiency.
3. Massive Scalability
- Can store billions of entities
- Automatically scales to handle large workloads
4. High Performance
- Optimized for fast read/write operations
- Best performance when querying by Partition Key and Row Key
5. Cost-Effective
- Low storage cost compared to relational databases
- Pay-per-use pricing model
Table Storage Structure
Azure Table Storage is organized as:
- Storage Account → top-level container
- Table → collection of entities
- Entity → a row of data
- Properties → attributes of an entity
💡 Example:
| PartitionKey | RowKey | Name | Age |
|---|---|---|---|
| Sales | 001 | John | 30 |
| Sales | 002 | Jane | 28 |
✔ Entities in the same table can have different properties.
Core Concepts
Partition Key
- Determines how data is distributed
- Improves scalability and performance
- Groups related data together
Row Key
- Unique identifier within a partition
- Used for fast lookups
Entity
- Equivalent to a row
- Contains key-value pairs (properties)
Common Use Cases
Azure Table Storage is ideal for:
- Storing large volumes of structured data
- User profiles or metadata
- IoT device data
- Application configuration data
- Log or telemetry data
✔ Best when simple, fast key-based access is needed.
Azure Table Storage vs Azure Cosmos DB (Table API)
This distinction is important for DP-900:
| Feature | Azure Table Storage | Azure Cosmos DB (Table API) |
|---|---|---|
| Performance | Standard | Higher performance |
| Global Distribution | Limited | Multi-region replication |
| SLA | Basic | Enterprise-grade |
| Cost | Lower | Higher |
✔ Cosmos DB is often used when global scale and advanced features are required.
When to Use Azure Table Storage
Use Table Storage when:
- You need NoSQL key-value storage
- Your data is structured but non-relational
- You require high scalability at low cost
- You can design around Partition Key / Row Key access patterns
When NOT to Use It
Avoid Table Storage when:
- You need complex queries or joins
- You require relational integrity
- You need advanced analytics capabilities
Why This Matters for DP-900
On the exam, you may be asked to:
- Identify Table Storage as a NoSQL key-value store
- Understand Partition Key and Row Key concepts
- Choose it for simple, scalable data storage scenarios
- Compare it with Blob Storage, Azure Files, or Cosmos DB
Summary — Exam-Relevant Takeaways
✔ Azure Table Storage = NoSQL key-value storage
✔ Stores structured, non-relational data
✔ Structure:
- Storage Account → Table → Entity → Properties
✔ Key concepts:
- Partition Key (grouping & scaling)
- Row Key (unique identifier)
✔ Benefits:
- Scalable
- Fast
- Cost-effective
✔ Best for:
- Large datasets with simple access patterns
- Key-based lookups
✔ Not suitable for:
- Complex queries or relational workloads
Go to the Practice Exam Questions for this topic.
Go to the DP-900 Exam Prep Hub main page.
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