# Publishing Batches of Events

Fiddler provides multiple options for publishing batches of production data, allowing you to choose the format and method that best suits your needs.

### Supported Data Formats

* pandas DataFrame
* CSV file (`.csv`),
* Parquet file (`.parquet`)

### Supported Data Locations

* In memory - pandas DataFrame
* Local disk - CSV, parquet

> **Note:**
>
> Fiddler's Python client offers the ability to integrate with Cloud data stores such as AWS S3. Refer to our [Integrations Guides](https://docs.fiddler.ai/integrations/) for examples.

### Batch Publishing Examples

Publish a batch of inference events using a parquet file, CSV file, or DataFrame using the Model.publish() function. When publishing in batch mode, `Model.publish()` executes asynchronously and returns a Job object. The job can be used to:

* Track by ID in the UI on the Jobs page
* Poll for status until completion
* Use the `wait()` method for synchronous behavior
* Log the job ID for reference

#### Parquet File

```python
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)
publish_job = model.publish('my_events_batch.parquet')

# The publish() method is asynchronous. Use the publish job's wait() method 
# if synchronous behavior is desired.
publish_job.wait() 
```

#### CSV File

```python
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)
publish_job = model.publish('my_events_batch.csv')
```

#### Pandas DataFrame

```python
import pandas as pd
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

my_events_df = pd.read_csv('my_events_batch.csv')
publish_job = model.publish(my_events_df)
```

*Please allow a few minutes for events to populate the related charts.* *Total processing time is a function of both width and count of the inference events.*


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