Nessie: 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 Nessie External Table with the StarTree controller REST APIs. There is no Data Portal wizard for Nessie yet — this API flow is the only way to onboard one today. Nessie tables are accessed over the same Iceberg REST protocol used for AWS Glue, Amazon S3 Tables, and Unity Catalog — but there’s no dedicated serviceType for Nessie. Use the generic spec-compliant adapter, catalogType=iceberg-rest, serviceType=rest, and address a specific branch/ref with catalog.iceberg-rest.prefix (Nessie’s version of a multi-catalog prefix). Data files must be Parquet.

Using AWS Glue, Amazon S3 Tables, or Unity Catalog instead? See their respective onboarding pages linked above.

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 data-plane base URL. Set export BASE_URL=https://dp.<data-plane-id>.cp.<region>.startree.cloud/api/pinot — the StarTree Cloud data-plane proxy that fronts the controller.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 Nessie server (self-hosted or managed) with its Iceberg REST API reachable — this is enabled by default on the official Nessie server/Docker image, served under <nessie-base-url>/iceberg.
The branch/ref to query (defaults to Nessie’s server-side default, usually main, if you don’t set one explicitly) and the namespace/table to onboard.
If your Nessie deployment sits behind auth (an OAuth2/OIDC proxy, or a static bearer token), have the credentials ready — see Authentication.
Read access to the underlying object storage. This connector supports S3-compatible storage for the data files.

Limitations

No dedicated serviceType for Nessie — it’s reached through the generic spec-compliant rest adapter, so there’s no Nessie-specific request validation; a misconfigured branch/ref prefix surfaces as a generic REST error, not a Nessie-specific one.
No credential vending. Unlike Unity Catalog, Nessie doesn’t automatically vend short-lived storage credentials — configure static keys, an assumed role, or the cluster’s node role explicitly.

Authentication

An External Table authenticates in two places, configured independently:

Catalog (REST) — keys under catalog.iceberg-rest.auth.rest.*. Choose one of three methods.
Storage (S3 data files) — keys under catalog.iceberg-rest.auth.storage.*. Choose one of three methods — same mechanism used for Glue and S3 Tables.

Catalog (REST) authentication: choose one

No authentication (default for self-hosted Nessie)

Most self-hosted Nessie deployments (e.g. the quickstart Docker image) run without REST auth. Omit auth.rest.* entirely.

{}

Bearer token

authType doesn’t need to be set explicitly — it’s auto-detected as token auth when .token is present.

{
  "catalog.iceberg-rest.auth.rest.token": "<bearer-token>"
}

OAuth2 client-credentials

Use this if your Nessie deployment sits behind an OAuth2/OIDC proxy (e.g. Keycloak) that requires a client-credentials grant. authType is optional; it’s auto-detected as oauth2 once the three required keys below are all present.

{
  "catalog.iceberg-rest.auth.rest.authType": "oauth2",
  "catalog.iceberg-rest.auth.rest.oauthTokenUri": "<oauth2-token-endpoint>",
  "catalog.iceberg-rest.auth.rest.oauthClientId": "<client-id>",
  "catalog.iceberg-rest.auth.rest.oauthClientSecret": "<client-secret>",
  "catalog.iceberg-rest.auth.rest.oauthScopes": "<space-separated-scopes-if-required>"
}

Storage authentication: choose one

Assumed IAM role (recommended for production)

No static secrets in the table config — every S3 read assumes roleArn via STS. Three things to know: auth.storage.region is required whenever a roleArn is set (the sync fails fast without it); any static auth.storage access keys are ignored once a roleArn is present; and the AssumeRole call itself authenticates with the cluster’s ambient AWS identity (node role / IRSA), so the target role’s trust policy must trust that identity (plus the externalId, if you set one).

{
  "catalog.iceberg-rest.auth.storage.roleArn": "arn:aws:iam::123456789012:role/pinot-external-table-reader",
  "catalog.iceberg-rest.auth.storage.externalId": "<external-id-if-your-trust-policy-requires-one>",
  "catalog.iceberg-rest.auth.storage.region": "<aws-region>"
}

Cluster node role

Omit every auth.storage.* credential key — only region is required. Credential resolution falls back to the AWS SDK default chain (the node’s instance profile / IRSA role), which must already have S3 access.

{
  "catalog.iceberg-rest.auth.storage.region": "<aws-region>"
}

Static access keys

Quick tests, or when role-based access isn’t available. Use the canonical accessKeyId / secretAccessKey spellings (an optional sessionToken is also accepted). Avoid the shorter accessKey/secretKey aliases: only part of the pipeline honors them (segment generation does, Iceberg manifest and data-file reads do not), so a short-alias config falls back to the cluster’s ambient credentials for catalog file reads and can fail in confusing ways.

{
  "catalog.iceberg-rest.auth.storage.accessKeyId": "<key>",
  "catalog.iceberg-rest.auth.storage.secretAccessKey": "<secret>",
  "catalog.iceberg-rest.auth.storage.region": "<aws-region>"
}

Selecting a branch or ref

catalog.iceberg-rest.prefix pins every request to a specific Nessie branch, tag, or ref (e.g. main, a feature branch, or a commit hash) — this is the same operator-supplied “prefix” mechanism generic multi-catalog Iceberg REST servers use, applied to Nessie’s branch model. Leave it unset to use the server’s default branch.

{
  "catalog.iceberg-rest.prefix": "main"
}

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.

curl -X POST "$BASE_URL/connections/browse" \
  -H "Content-Type: application/json" \
  -d '{
    "connection": {
      "type": "CATALOG",
      "params": {
        "catalogType": "iceberg-rest",
        "catalog.iceberg-rest.serviceType": "rest",
        "catalog.iceberg-rest.restUri": "http://<nessie-host>:19120/iceberg",
        "catalog.iceberg-rest.prefix": "main",
        "catalog.iceberg-rest.auth.storage.roleArn": "arn:aws:iam::123456789012:role/pinot-external-table-reader",
        "catalog.iceberg-rest.auth.storage.externalId": "<external-id-if-your-trust-policy-requires-one>",
        "catalog.iceberg-rest.auth.storage.region": "<aws-region>"
      }
    },
    "path": ""
  }'

Key	Value	Description
`catalog.iceberg-rest.restUri`	`http://<nessie-host>:19120/iceberg`	The Nessie server’s Iceberg REST endpoint.
`catalog.iceberg-rest.serviceType`	`rest`	Selects the generic spec-compliant adapter — there’s no Nessie-specific `serviceType`.
`catalog.iceberg-rest.prefix`	`main` (or any branch/ref)	Pins requests to a Nessie branch, tag, or ref.

Response

{
  "items": [
    { "name": "transportation", "type": "NAMESPACE" },
    { "name": "nyc_taxi_trips",  "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 "$BASE_URL/tables/preview" \
  -H "Content-Type: application/json" \
  -d '{
    "tableConfig": {
      "tableName": "nyc_taxi_trips_OFFLINE",
      "tableType": "OFFLINE",
      "task": {
        "taskTypeConfigsMap": {
          "ExternalTableSyncTask": {
            "catalogType": "iceberg-rest",
            "executor": "controller",
            "inputFormat": "parquet",
            "catalog.iceberg-rest.serviceType": "rest",
            "catalog.iceberg-rest.restUri": "http://<nessie-host>:19120/iceberg",
            "catalog.iceberg-rest.prefix": "main",
            "catalog.iceberg-rest.table.namespace": "transportation",
            "catalog.iceberg-rest.table.tableName": "nyc_taxi_trips",
            "catalog.iceberg-rest.auth.storage.roleArn": "arn:aws:iam::123456789012:role/pinot-external-table-reader",
            "catalog.iceberg-rest.auth.storage.externalId": "<external-id-if-your-trust-policy-requires-one>",
            "catalog.iceberg-rest.auth.storage.region": "<aws-region>"
          }
        }
      }
    },
    "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.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 "$BASE_URL/schemas" \
  -H "Content-Type: application/json" \
  -d @schema.json

{ "status": "nyc_taxi_trips 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 "$BASE_URL/tables" \
  -H "Content-Type: application/json" \
  -d @tableConfig.json

{ "status": "Table nyc_taxi_trips_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 "$BASE_URL/tasks/schedule?taskType=ExternalTableSyncTask&tableName=nyc_taxi_trips_OFFLINE"

Quickstart: onboard a table end-to-end

The whole flow as one script. Fill in the variables at the top, run the script, and the first sync starts automatically.

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

BASE_URL="https://dp.<data-plane-id>.cp.<region>.startree.cloud/api/pinot"
AUTH="Authorization: Bearer <token>"
REST_URI="http://<nessie-host>:19120/iceberg"
BRANCH="main"
NAMESPACE="transportation"
TABLE="nyc_taxi_trips"
STORAGE_ROLE_ARN="arn:aws:iam::123456789012:role/pinot-external-table-reader"
STORAGE_EXTERNAL_ID="<external-id-if-your-trust-policy-requires-one>"
STORAGE_REGION="<aws-region>"

# 1. Validate & browse root — lists Nessie namespaces on the selected branch.
curl -sf -X POST "$BASE_URL/connections/browse" \
  -H "$AUTH" -H 'Content-Type: application/json' \
  --data-raw "{
    \"connection\": {
      \"type\": \"CATALOG\",
      \"params\": {
        \"catalogType\": \"iceberg-rest\",
        \"catalog.iceberg-rest.serviceType\": \"rest\",
        \"catalog.iceberg-rest.restUri\": \"$REST_URI\",
        \"catalog.iceberg-rest.prefix\": \"$BRANCH\"
      }
    },
    \"path\": \"\"
  }" | jq .

# 2. Preview — infer schema + enriched table config.
curl -sf -X POST "$BASE_URL/tables/preview" \
  -H "$AUTH" -H 'Content-Type: application/json' \
  --data-raw "{
    \"tableConfig\": {
      \"tableName\": \"${TABLE}_OFFLINE\",
      \"tableType\": \"OFFLINE\",
      \"task\": {
        \"taskTypeConfigsMap\": {
          \"ExternalTableSyncTask\": {
            \"catalogType\": \"iceberg-rest\",
            \"executor\": \"controller\",
            \"inputFormat\": \"parquet\",
            \"catalog.iceberg-rest.serviceType\": \"rest\",
            \"catalog.iceberg-rest.restUri\": \"$REST_URI\",
            \"catalog.iceberg-rest.prefix\": \"$BRANCH\",
            \"catalog.iceberg-rest.table.namespace\": \"$NAMESPACE\",
            \"catalog.iceberg-rest.table.tableName\": \"$TABLE\",
            \"catalog.iceberg-rest.auth.storage.roleArn\": \"$STORAGE_ROLE_ARN\",
            \"catalog.iceberg-rest.auth.storage.externalId\": \"$STORAGE_EXTERNAL_ID\",
            \"catalog.iceberg-rest.auth.storage.region\": \"$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 "$BASE_URL/schemas" \
  -H "$AUTH" -H 'Content-Type: application/json' -d @schema.json

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

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

If your Nessie deployment requires REST auth, add the relevant catalog.iceberg-rest.auth.rest.* keys from Authentication to both requests above. 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 fileOnboardingRun.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 nyc_taxi_trips;

The count will be much larger than the preview’s summary.nSourceRows (preview only samples up to ~100 rows) — confirm it’s non-zero and plausible for your dataset. If the run 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.

Get Started

Ingestion

External Tables

Query Data

Manage Data

Observability

Visualize Data

Cluster Operations

Manage Security

Release Notes

Reference

Nessie: Onboarding via API

How it works

Prerequisites

Limitations

Authentication

Catalog (REST) authentication: choose one

No authentication (default for self-hosted Nessie)

Bearer token

OAuth2 client-credentials

Storage authentication: choose one

Assumed IAM role (recommended for production)

Cluster node role

Static access keys

Selecting a branch or ref

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

​Limitations

​Authentication

​Catalog (REST) authentication: choose one

​No authentication (default for self-hosted Nessie)

​Bearer token

​OAuth2 client-credentials

​Storage authentication: choose one

​Assumed IAM role (recommended for production)

​Cluster node role

​Static access keys

​Selecting a branch or ref

​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

Limitations

Authentication

Catalog (REST) authentication: choose one

No authentication (default for self-hosted Nessie)

Bearer token

OAuth2 client-credentials

Storage authentication: choose one

Assumed IAM role (recommended for production)

Cluster node role

Static access keys

Selecting a branch or ref

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