> ## Documentation Index
> Fetch the complete documentation index at: https://docs.featureform.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Scheduling Resources

> Often times we want to keep our Features and Training Sets up to date with the latest data. Featureform offers the ability to schedule and run updates for Transformations, Features, Labels, and Training Sets.

Scheduling can be done during the registration of a resource or modified afterwards.

Updates are atomic. Once the resource has been created initially, all updates to the resource will be generated in the background and will atomically replace the older version of the resource.

### Transformations

For transformations, the schedule can be specified in the decorator as a cron schedule.

```python theme={null}
@postgres.sql_transformation(variant="quickstart", schedule="* * * * *")
def average_user_transaction():
    return "SELECT CustomerID as user_id, avg(TransactionAmount) " \
           "as avg_transaction_amt from {{transactions.kaggle}} GROUP BY user_id"
```

### Features and labels

For features and labels, the schedule can be specified as an argument. Everything registered in the call will be run with the same schedule.

```python theme={null}
average_user_transaction.register_resources(
    entity=user,
    entity_column="user_id",
    inference_store=redis,
    features=[
        {"name": "avg_transactions", "variant": "quickstart", "column": "avg_transaction_amt", "type": "float32"},
    ],
    schedule="* * * * *"
)
```

### Training sets

For training sets, the schedule can be specified as an argument.

```python theme={null}
ff.register_training_set(
    "fraud_training", "quickstart",
    label=("fraudulent", "quickstart"),
    features=[("avg_transactions", "quickstart")],
    schedule="* * * * *"
)
```