For the complete documentation index, see llms.txt. This page is also available as Markdown.
Data Connections
How to get data into DBNL
Distributional is now Talaria Scientific. The DBNL product described in these docs has been sunset; this documentation is preserved for reference. Read the announcement.
Data Connections are how production AI log data is ingested into your DBNL Deployment as part of the Data Pipeline. Each Project has one ingestion method that is set at creation. If you need to change this later you can do this via the Project settings page.
Data Connections are how Production AI log data is ingested into DBNL, kicking off the Analytics Workflow.
Regardless of the data ingestion method, make sure your data adheres to the DBNL Semantic Convention to enable the richest analysis of the data.
Ingestion Type
Pros
Cons
OTEL Trace Ingestion
Get rich data logged in a few lines of embedded code
Enables full trace inspection in Logs page
Automatically maps to DBNL Semantic Convention if using standard semantic types
Cannot backfill data, requiring a full week before first Insights
SDK Log Ingestion
Can backfill previously collected OTLP trace data
Integrates into batch pipelines and orchestration jobs
Requires Python SDK code to be written and scheduled as part of external orchestration service
Managing Data Connections
Creating a New Data Connection
From the Namespace landing page click on "Data Connections" on the left panel. On the Data Connections landing page "+ Add Data Connection" in the upper right. Provide a required name for the Data Connection and an optional description. All Data Connections will be available to any User creating a Project in the Namespace.
