Azure Table Storage

What is Azure Table Storage?

Azure Table Storage is a NoSQL datastore that stores structured data as a collection of entities (rows) in tables. It is:

• Schema-less — Each entity can have different properties (columns)
• Massively scalable — Petabytes of data, millions of entities
• Very cheap — Much cheaper than Azure SQL Database for simple lookups
• Fast for key-based lookups — Extremely fast when you query by partition key + row key
• Not relational — No joins, no foreign keys, limited query capability

The Data Model

The hierarchy in Table Storage:

• Storage Account — Top-level container
• Table — A collection of entities (like a database table)
• Entity — A single record (like a database row), up to 1 MB
• Property — A name-value pair within an entity (like a column), up to 252 custom properties

System Properties

Every entity automatically has three system properties:

Example Entity

{
  "PartitionKey": "India",
  "RowKey": "user-001",
  "Name": "Priya Sharma",
  "Email": "priya@example.com",
  "Plan": "Premium",
  "CreatedDate": "2026-01-15",
  "Timestamp": "2026-05-07T10:30:00Z"
}

Partition Key and Row Key

The combination of PartitionKey + RowKey must be unique across the entire table. Together they form the primary key.

Partition Key

Determines which partition (storage node) an entity lives on. Entities with the same partition key are stored together and can be retrieved very efficiently. Good partition key design is crucial for performance:

• Good partition keys — CountryCode, UserId range, Date (year-month), TenantId
• Bad partition key — A single value for all entities (creates a hot partition — all traffic goes to one node)
• Bad partition key — Completely unique per entity (defeats the purpose of partitioning — poor scan performance)

Row Key

Uniquely identifies an entity within a partition. Combined with PartitionKey, it provides the fastest possible lookup — O(1) time complexity regardless of table size.

Querying with OData

Table Storage uses OData (Open Data Protocol) filter expressions for queries. Querying by PartitionKey + RowKey is fastest. Queries across all partitions (table scans) are slow and expensive for large tables.

# Query by PartitionKey + RowKey (fastest)
az storage entity show \
  --table-name users \
  --partition-key "India" \
  --row-key "user-001" \
  --account-name mystorageaccount2026 \
  --auth-mode login

# Query all entities in a partition
az storage entity query \
  --table-name users \
  --filter "PartitionKey eq 'India'" \
  --account-name mystorageaccount2026 \
  --auth-mode login

# Query with multiple filters
az storage entity query \
  --table-name users \
  --filter "PartitionKey eq 'India' and Plan eq 'Premium'" \
  --account-name mystorageaccount2026 \
  --auth-mode login

Working With Tables via CLI

# Create a table
az storage table create \
  --name users \
  --account-name mystorageaccount2026 \
  --auth-mode login

# Insert an entity
az storage entity insert \
  --table-name users \
  --entity PartitionKey=India RowKey=user-001 Name=PriyaSharma Email=priya@example.com Plan=Premium \
  --account-name mystorageaccount2026 \
  --auth-mode login

# Update an entity
az storage entity merge \
  --table-name users \
  --entity PartitionKey=India RowKey=user-001 Plan=Enterprise \
  --account-name mystorageaccount2026 \
  --auth-mode login

# Delete an entity
az storage entity delete \
  --table-name users \
  --partition-key India \
  --row-key user-001 \
  --account-name mystorageaccount2026 \
  --auth-mode login

Table Storage vs Cosmos DB

Azure Cosmos DB offers a Table API that is compatible with Azure Table Storage — you can migrate with minimal code changes. Cosmos DB is significantly more powerful but also more expensive:

When to Use Table Storage