Unity Catalog: Onboarding via API

This feature requires StarTree release 0.16.0 or later, and must be enabled on demand — contact StarTree support to activate it.

This page shows how to onboard a Unity Catalog External Table with the StarTree controller REST APIs. There is no Data Portal wizard for Unity Catalog yet — this API flow is the only way to onboard one today. Unity Catalog tables are accessed over the Iceberg REST protocol (catalogType=iceberg-rest, serviceType=unity), the same mechanism used for AWS Glue and Amazon S3 Tables — only the endpoint and auth differ. This works for both OSS Unity Catalog and Databricks-managed Unity Catalog, and for Delta Lake tables with UniForm enabled (UniForm publishes Iceberg-compatible metadata alongside the Delta log, so a Delta+UniForm table registered in Unity Catalog is queryable the same way as a native Iceberg table). Data files must be Parquet.

Known limitation: Delta tables with column mapping. Delta’s columnMapping.mode set to name or id — mandatory for Iceberg Compatibility V2, which covers most Databricks-managed UniForm tables — syncs successfully but currently fails segment load with org.apache.parquet.io.InvalidRecordException: <logical_name> not found in message spark_schema. A fix is planned but not yet available. Before onboarding a Databricks UniForm table, check its delta.columnMapping.mode table property; if it’s name or id, contact StarTree support to confirm current status before proceeding.

Using AWS Glue instead? See AWS Glue: Onboarding via API. Using Amazon S3 Tables instead? See Amazon S3 Tables: Onboarding via API.

How it works

Onboarding is four calls. Each one feeds the next:

Validate & browse   POST /connections/browse   →  validate; list namespaces/tables
Preview             POST /tables/preview       →  infer schema + enriched table config
Create schema       POST /schemas              →  register the schema
Create table        POST /tables               →  register the External Table

The preview call (step 2) is the core step: it samples the source, infers a Pinot schema, and returns a ready-to-use table config. Steps 3 and 4 just persist its output. Once the table exists, the controller’s watcher runs the first sync and re-syncs on schedule — no manual trigger. Track progress with the observability endpoints.

All paths are relative to your controller base URL. The examples assume export CONTROLLER=https://<your-controller>. On StarTree Cloud, the controller is reached through the data-plane proxy, so use export CONTROLLER=https://<data-plane-host>/api/pinot.If your controller requires authentication, add an Authorization header to every request — e.g. -H "Authorization: Bearer <token>". The examples below omit it for brevity.

Prerequisites

StarTree 0.16.0 or later with the External Table Beta feature enabled, and tiered storage configured on the cluster. Contact StarTree support if unsure.
Network access to the controller REST endpoint, plus an Authorization token if your cluster requires auth.
A running Unity Catalog:
- Databricks-managed — a workspace with Unity Catalog enabled, plus a personal access token (PAT) generated from the workspace (User Settings → Developer → Access tokens).
- OSS Unity Catalog — a reachable server. The OSS server is unauthenticated by default; no token is needed.
The Unity Catalog catalog name (used as warehouse below) and the namespace/table to onboard.
Read access to the underlying object storage. Unity Catalog vends short-lived storage credentials automatically for tables it manages, so static keys are usually unnecessary — see Authentication.

Authentication

An External Table authenticates in two places, configured independently:

Catalog (REST) — keys under catalog.iceberg-rest.auth.rest.*.
- Databricks (personal access token): set catalog.iceberg-rest.auth.rest.token to the PAT. authType doesn’t need to be set explicitly — it’s auto-detected as token auth when .token is present.
- Databricks (service principal / machine-to-machine, recommended for automated pipelines): use OAuth2 client-credentials instead of a PAT — see below.
- OSS Unity Catalog: omit auth.rest.* entirely.
Storage (S3 data files) — keys under catalog.iceberg-rest.auth.storage.*. Unity Catalog vends short-lived S3 credentials per table load, and those vended credentials take priority over anything set here — so accessKeyId/secretAccessKey can usually be left blank. Set them only if you need to override the vended credentials (e.g. a fixed IAM role for the tier’s deep-store access, separate from the source read path).

OAuth2 client-credentials (new in 0.16.0)

For Databricks service-principal / machine-to-machine access — the preferred method over a long-lived PAT for automated pipelines — set authType=oauth2 (or omit it; it’s auto-detected when all three required keys below are present) plus:

Key	Required	Description
`catalog.iceberg-rest.auth.rest.authType`	No	`oauth2` (auto-detected if the three keys below are all set).
`catalog.iceberg-rest.auth.rest.oauthTokenUri`	Yes	OAuth2 token endpoint.
`catalog.iceberg-rest.auth.rest.oauthClientId`	Yes	Service-principal client ID.
`catalog.iceberg-rest.auth.rest.oauthClientSecret`	Yes	Service-principal client secret.
`catalog.iceberg-rest.auth.rest.oauthScopes`	No	Space-separated OAuth2 scopes, if your Unity Catalog deployment requires them.

The client-credentials grant (RFC 6749) is exchanged for a short-lived token that’s cached and refreshed automatically ahead of expiry — you don’t need to rotate or refresh anything yourself. Note the SQL DDL shortcut for this same setup uses auth_type = 'oauth', which normalizes to this OAuth2 configuration.

`restUri` formats

Deployment	Example `restUri`
Databricks-managed Unity Catalog	`https://<workspace-host>/api/2.1/unity-catalog/iceberg-rest`
OSS Unity Catalog	`http://<host>:8080/api/2.1/unity-catalog/iceberg`

Both suffixes are exactly what you provide — StarTree doesn’t rewrite the host or path, it only appends the standard Iceberg REST /v1/... routes.

Step 1: Validate and browse the connection

POST /connections/browse There is no separate “validate” endpoint. Browsing the catalog is the validation step: a 200 with an items list (even an empty one) confirms your credentials and connectivity.

Set path to "" to browse the root — this both validates the connection and lists namespaces.
Set path to a namespace name to list the tables inside it.

Request

Field	Type	Required	Description
`connection.type`	string	Yes	Use `CATALOG` for all External Table sources.
`connection.params`	object	Yes	Catalog-specific connection settings (see example below).
`path`	string	No	Where to browse. `""` or omitted = root. A namespace name = that namespace’s tables.

Databricks-managed
OSS Unity Catalog

curl -X POST "$CONTROLLER/connections/browse" \
  -H "Content-Type: application/json" \
  -d '{
    "connection": {
      "type": "CATALOG",
      "params": {
        "catalogType": "iceberg-rest",
        "catalog.iceberg-rest.serviceType": "unity",
        "catalog.iceberg-rest.restUri": "https://<workspace-host>/api/2.1/unity-catalog/iceberg-rest",
        "catalog.iceberg-rest.warehouse": "<unity-catalog-name>",
        "catalog.iceberg-rest.auth.rest.token": "<databricks-pat>"
      }
    },
    "path": ""
  }'

curl -X POST "$CONTROLLER/connections/browse" \
  -H "Content-Type: application/json" \
  -d '{
    "connection": {
      "type": "CATALOG",
      "params": {
        "catalogType": "iceberg-rest",
        "catalog.iceberg-rest.serviceType": "unity",
        "catalog.iceberg-rest.restUri": "http://<host>:8080/api/2.1/unity-catalog/iceberg",
        "catalog.iceberg-rest.warehouse": "<unity-catalog-name>"
      }
    },
    "path": ""
  }'

Response

{
  "items": [
    { "name": "default", "type": "NAMESPACE" },
    { "name": "countries", "type": "TABLE" }
  ]
}

`items[].type`	Meaning
`NAMESPACE`	A namespace you can drill into — pass its `name` as `path`.
`TABLE`	A selectable table.

Step 2: Preview the schema

POST /tables/preview Samples the source, infers a Pinot schema, and returns an enriched table config (S3 tier, raw field configs, time column) plus sample rows. Review it, tweak if needed, then carry the schema and config forward to steps 3 and 4.

The request and response share the same JSON shape. You send a tableConfig describing the source; the response fills in schema, the enriched tableConfigs.offline, sampled rows, and a summary.

Request

Top-level fields:

Field	Type	Required	Description
`tableConfig`	object	Yes	A single OFFLINE table config whose `ExternalTableSyncTask` block describes the source.
`config`	object	No	Sampling and inference controls (see below). Defaults are applied if omitted.
`schema`	object	No	An explicit schema to use instead of inferring one.

config.inference — how the schema is derived:

Field	Type	Default	Description
`embeddedSchemaEnabled`	boolean	`false`	Read the schema embedded in the Iceberg metadata. Set this to `true` for External Tables.
`schemaInferenceEnabled`	boolean	`false`	Infer the schema from sampled rows. Fallback when no embedded schema exists.

Resolution order: explicit schema → embedded schema → inferred schema. config.sampling — how rows are sampled:

Field	Type	Default	Description
`min`	integer	`1`	Minimum records to sample.
`max`	integer	`100`	Maximum records to sample.

To list source files without sampling any data, set config.previewFiles.previewFilesOnly = true. The response returns matching file URIs in sourceFiles and skips schema inference. (OFFLINE only.)

curl -X POST "$CONTROLLER/tables/preview" \
  -H "Content-Type: application/json" \
  -d '{
    "tableConfig": {
      "tableName": "countries_OFFLINE",
      "tableType": "OFFLINE",
      "task": {
        "taskTypeConfigsMap": {
          "ExternalTableSyncTask": {
            "catalogType": "iceberg-rest",
            "executor": "controller",
            "inputFormat": "parquet",
            "catalog.iceberg-rest.serviceType": "unity",
            "catalog.iceberg-rest.restUri": "https://<workspace-host>/api/2.1/unity-catalog/iceberg-rest",
            "catalog.iceberg-rest.warehouse": "<unity-catalog-name>",
            "catalog.iceberg-rest.table.namespace": "default",
            "catalog.iceberg-rest.table.tableName": "countries",
            "catalog.iceberg-rest.auth.rest.token": "<databricks-pat>",
            "catalog.iceberg-rest.auth.storage.region": "us-west-1"
          }
        }
      }
    },
    "config": { "inference": { "embeddedSchemaEnabled": true } }
  }'

Setting the namespace and table. Take them from the browse response — set catalog.iceberg-rest.table.namespace / .tableName to the NAMESPACE and TABLE you selected.auth.storage.region is required even when relying on Unity’s vended storage credentials (see Authentication) — it tells the S3 client which region to sign requests for.Use executor: controller — it’s required for the controller-watcher flow and the observability endpoints. The input tableConfig is intentionally minimal; /tables/preview returns the complete tableConfigs.offline you persist in Step 4.

Response

Field	Type	Description
`schema`	object	The resolved Pinot schema. Use this in step 3.
`tableConfigs.offline`	object	The enriched OFFLINE table config, ready to persist. Use this in step 4. Secrets are masked as `*****`.
`rows`	array	Sample records after schema + ingestion transforms.
`sourceRows`	array	Raw records before transformation.
`summary`	object	Run summary: `nSourceRows`, `nRows`, `nColumns`, and `summary.batch.nMatchingFiles` (files found).

Adjust the schema (time column, column names, null handling) before moving on.

Step 3: Create the schema

POST /schemas Send the schema object from the preview response. Rename its schemaName to match your table.

curl -X POST "$CONTROLLER/schemas" \
  -H "Content-Type: application/json" \
  -d @schema.json

{ "status": "countries successfully added" }

Step 4: Create the table

POST /tables Send the enriched tableConfigs.offline object from the preview response. Its ExternalTableSyncTask block is what marks the table as external.

curl -X POST "$CONTROLLER/tables" \
  -H "Content-Type: application/json" \
  -d @tableConfig.json

{ "status": "Table countries_OFFLINE successfully added" }

The controller’s External Table watcher then discovers the table, runs the first sync, and re-syncs at the schedule (cron) interval. There is no separate start call.

The first sync runs on the watcher’s next tick. To kick it off immediately instead of waiting, you can manually trigger a run:

curl -X POST "$CONTROLLER/tasks/schedule?taskType=ExternalTableSyncTask&tableName=countries_OFFLINE"

Quickstart: onboard a table end-to-end

The whole flow as one script (Databricks-managed Unity Catalog). Fill in the variables at the top, run the script, and the first sync starts automatically. schema.json and tableConfig.json — referenced in steps 3 and 4 above — are extracted here from the preview response with jq.

#!/usr/bin/env bash
set -euo pipefail

CONTROLLER="https://<your-controller>"
AUTH="Authorization: Bearer <token>"
REST_URI="https://<workspace-host>/api/2.1/unity-catalog/iceberg-rest"
WAREHOUSE="<unity-catalog-name>"
NAMESPACE="default"
TABLE="countries"
PAT="<databricks-pat>"
STORAGE_REGION="us-west-1"

# 1. Validate & browse root — lists Unity Catalog namespaces.
curl -sf -X POST "$CONTROLLER/connections/browse" \
  -H "$AUTH" -H 'Content-Type: application/json' \
  --data-raw "{
    \"connection\": {
      \"type\": \"CATALOG\",
      \"params\": {
        \"catalogType\": \"iceberg-rest\",
        \"catalog.iceberg-rest.serviceType\": \"unity\",
        \"catalog.iceberg-rest.restUri\": \"$REST_URI\",
        \"catalog.iceberg-rest.warehouse\": \"$WAREHOUSE\",
        \"catalog.iceberg-rest.auth.rest.token\": \"$PAT\"
      }
    },
    \"path\": \"\"
  }" | jq .

# 2. Preview — infer schema + enriched table config.
curl -sf -X POST "$CONTROLLER/tables/preview" \
  -H "$AUTH" -H 'Content-Type: application/json' \
  --data-raw "{
    \"tableConfig\": {
      \"tableName\": \"${TABLE}_OFFLINE\",
      \"tableType\": \"OFFLINE\",
      \"task\": {
        \"taskTypeConfigsMap\": {
          \"ExternalTableSyncTask\": {
            \"catalogType\": \"iceberg-rest\",
            \"executor\": \"controller\",
            \"inputFormat\": \"parquet\",
            \"catalog.iceberg-rest.serviceType\": \"unity\",
            \"catalog.iceberg-rest.restUri\": \"$REST_URI\",
            \"catalog.iceberg-rest.warehouse\": \"$WAREHOUSE\",
            \"catalog.iceberg-rest.table.namespace\": \"$NAMESPACE\",
            \"catalog.iceberg-rest.table.tableName\": \"$TABLE\",
            \"catalog.iceberg-rest.auth.rest.token\": \"$PAT\",
            \"catalog.iceberg-rest.auth.storage.region\": \"$STORAGE_REGION\"
          }
        }
      }
    },
    \"config\": { \"inference\": { \"embeddedSchemaEnabled\": true } }
  }" > preview.json

# 3. Extract schema and table config from preview response.
jq --arg t "$TABLE" '.schema | .schemaName = $t' preview.json > schema.json
jq '.tableConfigs.offline'                        preview.json > tableConfig.json

# 4. Create the schema.
curl -sf -X POST "$CONTROLLER/schemas" \
  -H "$AUTH" -H 'Content-Type: application/json' -d @schema.json

# 5. Create the table — watcher starts the first sync automatically.
curl -sf -X POST "$CONTROLLER/tables" \
  -H "$AUTH" -H 'Content-Type: application/json' -d @tableConfig.json

# 6. (optional) Poll until sync completes.
curl -sf "$CONTROLLER/tables/${TABLE}_OFFLINE/externalTable/status" -H "$AUTH" \
  | jq -e '.status == "COMPLETED" and .segmentsUploaded == .filesDiscovered'

For OSS Unity Catalog, drop the PAT variable and the auth.rest.token key from both requests, and point REST_URI at your server (e.g. http://<host>:8080/api/2.1/unity-catalog/iceberg). Once step 6 exits 0, verify it’s queryable.

Monitor onboarding

Three read-only endpoints report ingestion progress — run status, ingestion checkpoint, and source file count — and require executor=controller (set automatically). See Observability for full request and response details.

Verify it’s queryable

When status is COMPLETED and segmentsUploaded matches filesDiscovered, run a query against the broker (or the Data Portal query console) to confirm the data is live:

SELECT count(*) FROM countries;

If status is COMPLETED but the count is 0, give segments a moment to load on the servers, then recheck; if it persists, see Troubleshooting.

What’s next

Now that the table is created and data is loading, these are the highest-impact follow-up steps:

Add indexes for your query patterns. Without indexes, every query scans all remote Parquet data. Add a range index on time/numeric columns, an inverted index on low-cardinality filter columns, and a bloom filter on high-cardinality ID columns. → Indexes
Enable caching and preload. Set enable.prefetch.page.cache=true and preload.enable=true on the S3 tier so index data is served from local disk on repeated queries instead of re-fetched from S3. → Data and Index Caching
Protect large-scan queries from OOM. For tables that receive heavy aggregations or wide scans, enable the query OOM killer so a runaway query is killed instead of crashing the server. → Best Practices & Configs — Query OOM protection
Monitor ongoing syncs. Use the observability endpoints to check run status, ingestion checkpoint, and source file count after each scheduled sync. → Observability

For common questions and failures, see the FAQ and Troubleshooting.

​How it works

​Prerequisites

​Authentication

​OAuth2 client-credentials (new in 0.16.0)

​restUri formats

​Step 1: Validate and browse the connection

​Request

​Response

​Step 2: Preview the schema

​Request

​Response

​Step 3: Create the schema

​Step 4: Create the table

​Quickstart: onboard a table end-to-end

​Monitor onboarding

​Verify it’s queryable

​What’s next

How it works

Prerequisites

Authentication

OAuth2 client-credentials (new in 0.16.0)

`restUri` formats

Step 1: Validate and browse the connection

Request

Response

Step 2: Preview the schema

Request

Response

Step 3: Create the schema

Step 4: Create the table

Quickstart: onboard a table end-to-end

Monitor onboarding

Verify it’s queryable

What’s next