azure-data-tables-py
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".
Best use case
azure-data-tables-py is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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".
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".
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "azure-data-tables-py" skill to help with this workflow task. Context: 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".
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-data-tables-py/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-data-tables-py Compares
| Feature / Agent | azure-data-tables-py | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Azure Tables SDK for Python
NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).
## Installation
```bash
pip install azure-data-tables azure-identity
```
## Environment Variables
```bash
# Azure Storage Tables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.table.core.windows.net
# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://<account>.table.cosmos.azure.com
```
## Authentication
```python
from azure.identity import DefaultAzureCredential
from azure.data.tables import TableServiceClient, TableClient
credential = DefaultAzureCredential()
endpoint = "https://<account>.table.core.windows.net"
# Service client (manage tables)
service_client = TableServiceClient(endpoint=endpoint, credential=credential)
# Table client (work with entities)
table_client = TableClient(endpoint=endpoint, table_name="mytable", credential=credential)
```
## Client Types
| Client | Purpose |
|--------|---------|
| `TableServiceClient` | Create/delete tables, list tables |
| `TableClient` | Entity CRUD, queries |
## Table Operations
```python
# 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
```python
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
```python
# Get by key (fastest)
entity = table_client.get_entity(
partition_key="sales",
row_key="order-001"
)
print(f"Product: {entity['product']}")
```
### Update Entity
```python
# 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
```python
table_client.delete_entity(
partition_key="sales",
row_key="order-001"
)
```
## Query Entities
### Query Within Partition
```python
# Query by partition (efficient)
entities = table_client.query_entities(
query_filter="PartitionKey eq 'sales'"
)
for entity in entities:
print(entity)
```
### Query with Filters
```python
# 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
```python
entities = table_client.query_entities(
query_filter="PartitionKey eq 'sales'",
select=["RowKey", "product", "price"]
)
```
### List All Entities
```python
# List all (cross-partition - use sparingly)
for entity in table_client.list_entities():
print(entity)
```
## Batch Operations
```python
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
```python
from azure.data.tables.aio import TableServiceClient, TableClient
from azure.identity.aio import DefaultAzureCredential
async def table_operations():
credential = DefaultAzureCredential()
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
1. **Design partition keys** for query patterns and even distribution
2. **Query within partitions** whenever possible (cross-partition is expensive)
3. **Use batch operations** for multiple entities in same partition
4. **Use `upsert_entity`** for idempotent writes
5. **Use parameterized queries** to prevent injection
6. **Keep entities small** — max 1MB per entity
7. **Use async client** for high-throughput scenariosRelated Skills
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