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Azure Data Tables Py

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Tool integration
  • Software development
  • MIT
  • Low risk

From the author

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".
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The azure-data-tables-py skill provides guidance and code patterns for using the Azure Tables SDK for Python. It covers connecting to Azure Storage Tables or the Cosmos DB Table API, managing tables, performing CRUD operations on entities, and executing batch transactions. The documentation emphasizes secure authentication using DefaultAzureCredential and the use of context managers for resource lifecycle management.

What it does

  • Provides patterns for creating, listing, and deleting tables.
  • Demonstrates entity CRUD operations including create, upsert, update, and delete.
  • Shows how to query entities using filters and property selection.
  • Explains how to perform batch transactions within a single partition.
  • Includes examples for both synchronous and asynchronous client implementations.
  • Maps Python data types to Azure Table storage types.

When to use it

  • When you need to store structured NoSQL key-value data in Azure.
  • When building applications that require integration with Azure Storage Tables or Cosmos DB Table API.
  • When you need to implement secure, identity-based authentication for Azure data services.
  • When you require both synchronous and asynchronous access patterns for table operations.

Advantages

  • Supports both Azure Storage Tables and Cosmos DB Table API.
  • Promotes secure authentication practices using DefaultAzureCredential.
  • Provides clear patterns for resource cleanup using context managers.
  • Includes support for both sync and async programming models.
  • Offers parameterized query support to help prevent injection.

Usage examples

“How do I create a new entity in an Azure Table using Python?”
The agent provides a code snippet using TableClient.create_entity, including the required PartitionKey and RowKey fields.
“Show me how to query entities in a specific partition.”
The agent provides a code example using table_client.query_entities with a filter string for the PartitionKey.
“How do I perform a batch operation in Azure Tables?”
The agent provides a code block demonstrating how to use submit_transaction with a list of operations for a single partition.
“How should I authenticate my Python application to Azure Tables?”
The agent explains the use of DefaultAzureCredential and provides examples for setting up the client with this credential.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/azure-data-tables-py/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-data-tables-py.zip -d .agents/skills

Documentation by Microsoft

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:

  1. 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: DefaultAzureCredential works as-is.

    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.

  2. 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: and async with DefaultAzureCredential() as credential: (from azure.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

ClientPurpose
TableServiceClientCreate/delete tables, list tables
TableClientEntity 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 TypeTable Storage Type
strString
intInt64
floatDouble
boolBoolean
datetimeDateTime
bytesBinary
UUIDGuid

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.data.tables sync clients with azure.data.tables.aio async clients in the same call path. Choose one mode per module.

  2. Always use context managers for clients and async credentials. Wrap every client in with TableClient(...) as client: (sync) or async with TableClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.

  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).

  4. Design partition keys for query patterns and even distribution

  5. Query within partitions whenever possible (cross-partition is expensive)

  6. Use batch operations for multiple entities in same partition

  7. Use upsert_entity for idempotent writes

  8. Use parameterized queries to prevent injection

  9. Keep entities small — max 1MB per entity

  10. Use async client for high-throughput scenarios

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-data-tables-py · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-data-tables-py.zip