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This feature is available starting in StarTree release 0.16.0. It must be enabled on demand — contact your StarTree representative to have it activated for your environment.
This guide walks through connecting a Unity Catalog to StarTree using the Data Portal UI. No API calls or JSON configuration required — Data Portal guides you through catalog connection, table selection, and onboarding setup through a point-and-click interface. Unity Catalog tables are accessed over the Iceberg REST protocol, and this path also covers Delta Lake tables with UniForm enabled registered in the catalog. This works for both Databricks-managed and OSS Unity Catalog. Data files must be Parquet.
Known limitation: Delta tables with column mapping. Delta tables whose delta.columnMapping.mode is name or id — which covers most Databricks-managed UniForm tables — sync successfully but currently fail segment load. Before onboarding a Databricks UniForm table, check its delta.columnMapping.mode table property and contact StarTree support to confirm current status. See Onboarding via API — Limitations for details.
Looking for the API-based approach? See Unity Catalog: Onboarding via API. Using AWS Glue instead? See AWS Glue: Onboarding via Data Portal. Using Amazon S3 Tables instead? See Amazon S3 Tables: Onboarding via Data Portal.

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Prerequisites

Before starting, ensure you have:
  • StarTree 0.16.0 or later with the external table Beta feature enabled and tiered storage configured for your environment. Contact StarTree support if unsure.
  • A running Unity Catalog:
    • Databricks-managed — a workspace with Unity Catalog enabled, plus either a personal access token (PAT) or OAuth2 service-principal credentials (client ID + secret) to authenticate with.
    • OSS Unity Catalog — a reachable server. The OSS server is unauthenticated by default, so no token is needed.
  • The Unity Catalog catalog name (used as the warehouse identifier) and the namespace/table you want to onboard.
  • Read access to the underlying S3 object storage. Unity Catalog vends short-lived storage credentials automatically for tables it manages, so static keys are usually unnecessary — but the storage region is always required.
  • For Delta Lake tables: UniForm must be enabled on the source table so it’s exposed over the Iceberg REST endpoint. See Onboarding via API — Prerequisites for the exact table properties and Databricks runtime requirements.

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Step 1: Open the External Tables

  1. Log in to Data Portal.
  2. In the left navigation, go to Tables.
  3. Click + Connect External Table.
The wizard opens with a connection configuration screen.

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Step 2: Configure the Unity Catalog Connection

Choose Unity Catalog as the catalog type. It now appears as a top-level catalog option, alongside AWS Glue and Amazon S3 Tables, rather than being nested under a generic Iceberg REST grouping. Fill in the credentials and connection details: Details Metastore — how Data Portal authenticates with the Unity Catalog Iceberg REST API. For Personal Access Token (PAT): For OAuth2 (Databricks service principal — recommended over a long-lived PAT for automated pipelines, new in 0.16.0): Storage — credentials for reading the underlying Parquet data files. Unity Catalog vends short-lived storage credentials automatically for most tables, so these can typically be left blank; only Region is always required. Click Validate Connection. Data Portal calls the catalog’s validate endpoint and confirms credentials and connectivity before proceeding.

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Step 3: Browse and Select a Table

Once the connection is validated:
  1. Data Portal lists the available namespaces in the Unity Catalog.
  2. Select a namespace to expand its tables.
  3. Click the table you want to onboard.
Data Portal reads the Iceberg schema and derives a Pinot schema automatically.

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Step 4: Review the Schema

The auto-generated Pinot schema is displayed for review. You can:
  • Set a time column — select the column to use as the Pinot time dimension (optional; leave blank for no time partitioning).
  • Include or exclude partition columns — toggle whether Iceberg partition columns are added as Pinot dimension columns.
  • Rename the schema — provide a custom schema name, or accept the default derived from the table name.
Click Next when the schema looks correct.

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Step 5: Configure the Table

Review and adjust the table configuration: Click Create Table to register the schema and table with Pinot. Data Portal automatically triggers the first onboarding run immediately after creation — no manual step required.

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Step 6: Monitor Onboarding

Once the table is created, onboarding starts automatically. The table detail view shows the status in real time:
  • Running — the task is actively reading Iceberg snapshots and building Pinot segments.
  • Completed — onboarding finished successfully. The last ingested snapshot ID is shown.
  • Failed — onboarding encountered an error. The error message and the number of files discovered vs. segments generated are surfaced to help diagnose the issue.
For deeper observability — watcher status, checkpoint values, and per-snapshot file counts — see the Observability page.

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Pausing Onboarding

To pause scheduled onboarding from Data Portal:
  1. Open the table in the Tables view.
  2. Click Pause Sync. This removes the schedule (cron trigger) from the table’s ExternalTableSyncTask config and stashes its value under schedule.paused, so the watcher stops firing new runs. Any run currently in progress completes normally, and existing segments and the last checkpoint are untouched. Click Resume Sync to restore the schedule from schedule.paused — onboarding resumes from where it left off; it does not restart from scratch.
This is a separate mechanism from the enabled key documented in Best Practices & Configs — that key can also be set to false directly via the table config or API to stop new runs, but the Data Portal’s Pause Sync button acts on schedule/schedule.paused, not enabled.

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Frequently Asked Questions

The Validate step fails — what should I check?
  • For Databricks with PAT: confirm the token is valid and hasn’t expired, and that the workspace has Unity Catalog enabled.
  • For Databricks with OAuth2: confirm the token URI, client ID, and client secret are correct, and that the service principal has access to the catalog.
  • For OSS Unity Catalog: confirm the server is reachable from the cluster and the REST URI includes the correct path suffix (/api/2.1/unity-catalog/iceberg).
  • Verify the warehouse value matches the Unity Catalog catalog name exactly.
Table load or sync fails even though browsing worked — what should I check?
  • Confirm the storage Region is set — it’s required even when relying on Unity’s vended credentials, which don’t carry a region.
  • On Databricks, credential vending requires external data access enabled on the metastore and the EXTERNAL USE SCHEMA privilege on the schema for the principal behind your PAT or OAuth2 credentials. Without them, catalog browsing works but table loads fail. See Onboarding via API — Storage authentication.
Can I onboard multiple tables from the same catalog? Yes. After creating the first table, start the wizard again and reuse the same connection credentials. Each table is registered as an independent Pinot table with its own onboarding schedule.
The table was created but onboarding hasn’t started — what should I check? Data Portal triggers the first onboarding run automatically after table creation. If onboarding hasn’t started, check the table’s detail page for an error status and review the error message. You can also trigger a run manually via the trigger API.