azure-data-tables-py
Connect to and manage Azure Table storage using Python. Supports entity CRUD, batch operations, and NoSQL key-value storage.
Install
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Activation
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Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations. Triggers: "table storage", "TableServiceClient", "TableClient", "entities", "PartitionKey", "RowKey".Key capabilities
- →Execute entity CRUD operations
- →Perform batch transactions
- →Query entities with filters
- →Manage table storage and Cosmos DB table APIs
How it works
The SDK uses TableServiceClient and TableClient to interact with structured NoSQL data, requiring PartitionKey and RowKey for unique identification.
Inputs & outputs
When to use azure-data-tables-py
- →Create and store entities in Azure Tables
- →Retrieve data using PartitionKey and RowKey
- →Execute batch operations on table entities
- →Connect Python apps to Cosmos DB table endpoints
About this skill
Azure Tables SDK for Python
NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).
Installation
pip install azure-data-tables azure-identity
Environment Variables
# Azure Storage Tables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.table.core.windows.net # Required for Azure Storage Tables
# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://<account>.table.cosmos.azure.com # Required for Cosmos DB Table API
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
Authentication & Lifecycle
🔑 Two rules apply to every code sample below:
- Prefer
DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
- Local dev:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
- Sync:
with <Client>(...) as client:- Async:
async with <Client>(...) as client:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.data.tables import TableServiceClient, TableClient
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
endpoint = "https://<account>.table.core.windows.net"
# Service client (manage tables)
with TableServiceClient(endpoint=endpoint, credential=credential) as service_client:
# Use service_client here (see following sections for operations)
...
# Table client (work with entities)
with TableClient(endpoint=endpoint, table_name="mytable", credential=credential) as table_client:
# Use table_client here (see following sections for operations)
...
Client Types
| Client | Purpose |
|---|---|
TableServiceClient | Create/delete tables, list tables |
TableClient | Entity CRUD, queries |
Table Operations
# Create table
service_client.create_table("mytable")
# Create if not exists
service_client.create_table_if_not_exists("mytable")
# Delete table
service_client.delete_table("mytable")
# List tables
for table in service_client.list_tables():
print(table.name)
# Get table client
table_client = service_client.get_table_client("mytable")
Entity Operations
Important: Every entity requires PartitionKey and RowKey (together form unique ID).
Create Entity
entity = {
"PartitionKey": "sales",
"RowKey": "order-001",
"product": "Widget",
"quantity": 5,
"price": 9.99,
"shipped": False
}
# Create (fails if exists)
table_client.create_entity(entity=entity)
# Upsert (create or replace)
table_client.upsert_entity(entity=entity)
Get Entity
# Get by key (fastest)
entity = table_client.get_entity(
partition_key="sales",
row_key="order-001"
)
print(f"Product: {entity['product']}")
Update Entity
# Replace entire entity
entity["quantity"] = 10
table_client.update_entity(entity=entity, mode="replace")
# Merge (update specific fields only)
update = {
"PartitionKey": "sales",
"RowKey": "order-001",
"shipped": True
}
table_client.update_entity(entity=update, mode="merge")
Delete Entity
table_client.delete_entity(
partition_key="sales",
row_key="order-001"
)
Query Entities
Query Within Partition
# Query by partition (efficient)
entities = table_client.query_entities(
query_filter="PartitionKey eq 'sales'"
)
for entity in entities:
print(entity)
Query with Filters
# Filter by properties
entities = table_client.query_entities(
query_filter="PartitionKey eq 'sales' and quantity gt 3"
)
# With parameters (safer)
entities = table_client.query_entities(
query_filter="PartitionKey eq @pk and price lt @max_price",
parameters={"pk": "sales", "max_price": 50.0}
)
Select Specific Properties
entities = table_client.query_entities(
query_filter="PartitionKey eq 'sales'",
select=["RowKey", "product", "price"]
)
List All Entities
# List all (cross-partition - use sparingly)
for entity in table_client.list_entities():
print(entity)
Batch Operations
from azure.data.tables import TableTransactionError
# Batch operations (same partition only!)
operations = [
("create", {"PartitionKey": "batch", "RowKey": "1", "data": "first"}),
("create", {"PartitionKey": "batch", "RowKey": "2", "data": "second"}),
("upsert", {"PartitionKey": "batch", "RowKey": "3", "data": "third"}),
]
try:
table_client.submit_transaction(operations)
except TableTransactionError as e:
print(f"Transaction failed: {e}")
Async Client
from azure.data.tables.aio import TableServiceClient, TableClient
from azure.identity.aio import DefaultAzureCredential
async def table_operations():
async with DefaultAzureCredential() as credential:
async with TableClient(
endpoint="https://<account>.table.core.windows.net",
table_name="mytable",
credential=credential
) as client:
# Create
await client.create_entity(entity={
"PartitionKey": "async",
"RowKey": "1",
"data": "test"
})
# Query
async for entity in client.query_entities("PartitionKey eq 'async'"):
print(entity)
import asyncio
asyncio.run(table_operations())
Data Types
| Python Type | Table Storage Type |
|---|---|
str | String |
int | Int64 |
float | Double |
bool | Boolean |
datetime | DateTime |
bytes | Binary |
UUID | Guid |
Best Practices
- Pick sync OR async and stay consistent. Do not mix
azure.data.tablessync clients withazure.data.tables.aioasync clients in the same call path. Choose one mode per module. - Always use context managers for clients and async credentials. Wrap every client in
with TableClient(...) as client:(sync) orasync with TableClient(...) as client:(async). For asyncDefaultAzureCredentialfromazure.identity.aio, also useasync with credential:so tokens and transports are cleaned up. - Use
DefaultAzureCredentialfor portable auth across local dev and Azure (avoid connection strings / API keys when possible). - Design partition keys for query patterns and even distribution
- Query within partitions whenever possible (cross-partition is expensive)
- Use batch operations for multiple entities in same partition
- Use
upsert_entityfor idempotent writes - Use parameterized queries to prevent injection
- Keep entities small — max 1MB per entity
- Use async client for high-throughput scenarios
Reference Files
| File | Contents |
|---|---|
| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
When not to use it
- →When requiring complex relational joins
- →When exceeding 1MB entity size limits
Prerequisites
Limitations
- →Batch operations restricted to same partition only
- →Entities limited to 1MB
How it compares
It enforces context manager usage for deterministic resource release compared to manual connection handling.
Compared to similar skills
azure-data-tables-py side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| azure-data-tables-py (this skill) | 1 | 29d | Review | Beginner |
| django-pro | 20 | 4mo | No flags | Intermediate |
| senior-backend | 14 | 7mo | Review | Advanced |
| cloudbase-guidelines | 1 | 3mo | No flags | Intermediate |
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