---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 55f4db58b288178cf2748bc4b20c9d8c9468ca0bf8acb150851c4d7072bd303c
  pageDirectory: concepts
  sources:
    - archive-traces-to-a-delta-table-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - unity-catalog-delta-table-as-trace-sink
    - UCDTATS
  citations:
    - file: archive-traces-to-a-delta-table-databricks-on-aws.md
title: Unity Catalog Delta Table as Trace Sink
description: Using a Unity Catalog-managed Delta table (specified with catalog.schema.table notation) as the storage target for archived MLflow traces, with automatic table creation and append behavior.
tags:
  - unity-catalog
  - delta-table
  - storage
  - databricks
timestamp: "2026-06-18T14:27:11.256Z"
---

# Unity Catalog Delta Table as Trace Sink

**Unity Catalog Delta Table as Trace Sink** refers to the capability to save [traces](/concepts/mlflow-tracing.md) and their associated assessments to a [Unity Catalog](/concepts/unity-catalog.md) [Delta Table](/concepts/delta-lake-table.md) for long-term storage, advanced analysis, and durable record-keeping of application behavior.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

## Overview

When you configure a Unity Catalog Delta table as a trace sink, all traces generated by your application are automatically written to the specified table. This enables building custom dashboards, performing in-depth analytics on trace data, and maintaining a permanent history of your application's behavior beyond the default retention period.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

The target table will be created if it does not already exist. If the table already exists, new traces are appended to it.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

## Requirements

You must have the necessary permissions to write to the specified Unity Catalog Delta table. The table's full name must include the [Catalog and Schema](/concepts/catalog-and-schema.md) (e.g., `my_catalog.my_schema.archived_traces`).^[archive-traces-to-a-delta-table-databricks-on-aws.md]

## Enabling Trace Archiving

### Using the MLflow API

To begin archiving traces for an experiment, use the `enable_databricks_trace_archival` function from `mlflow.tracing.archival`. You must specify the full name of the target Delta table. If you don't provide an `experiment_id`, archiving is enabled for the currently active experiment.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

```python
from mlflow.tracing.archival import enable_databricks_trace_archival

# Archive traces from a specific experiment to a Unity Catalog Delta table
enable_databricks_trace_archival(
    delta_table_fullname="my_catalog.my_schema.archived_traces",
    experiment_id="YOUR_EXPERIMENT_ID",
)
```

### Using the Databricks UI

Trace archiving can also be configured through the Databricks UI for production monitoring setups.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

## Disabling Trace Archiving

Stop archiving traces for an experiment at any time using the `disable_databricks_trace_archival` function.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

```python
from mlflow.tracing.archival import disable_databricks_trace_archival

# Stop archiving traces for the specified experiment
disable_databricks_trace_archival(experiment_id="YOUR_EXPERIMENT_ID")
```

## Use Cases

- **Custom dashboards** — Build visualizations on top of archived trace data for monitoring application performance.^[archive-traces-to-a-delta-table-databricks-on-aws.md]
- **In-depth analytics** — Perform advanced analysis on trace data using SQL or Python.^[archive-traces-to-a-delta-table-databricks-on-aws.md]
- **Durable record** — Maintain a permanent, queryable history of your application's behavior.^[archive-traces-to-a-delta-table-databricks-on-aws.md]
- **Evaluation datasets** — Use archived traces to build [MLflow Evaluation Datasets](/concepts/evaluation-datasets.md) for offline testing.^[archive-traces-to-a-delta-table-databricks-on-aws.md]

## Related Concepts

- [MLflow Tracing](/concepts/mlflow-tracing.md) — The system that generates the traces being archived
- [Production Monitoring for GenAI](/concepts/production-quality-monitoring-mlflow-genai.md) — Setting up production monitoring for GenAI applications
- [MLflow Evaluation](/concepts/mlflow-evaluation-ui.md) — Using traces to build evaluation datasets
- [Delta Table](/concepts/delta-lake-table.md) — The underlying storage format for archived traces
- [Unity Catalog](/concepts/unity-catalog.md) — The governance layer for the Delta table

## Sources

- archive-traces-to-a-delta-table-databricks-on-aws.md

# Citations

1. [archive-traces-to-a-delta-table-databricks-on-aws.md](/references/archive-traces-to-a-delta-table-databricks-on-aws-85660ef9.md)
