Polaris setup tools can export catalog configuration to YAML, validate a proposed file with dry-run, and apply supported resources repeatedly. Use this when you want reviewed bootstrap and environment promotion from staging to production. But understand a hard limit up front: the setup apply command is create-oriented and will skip resources that already exist. That means this is not a full state reconciler. Operators still must detect drift, handle updates explicitly, and protect sensitive data in exported files.
The Polaris CLI setup tools provide three core operations documented in the Polaris 1.8.0 setup tools guide and the CLI reference. You can export an existing catalog to YAML, you can validate a YAML bundle with a dry-run mode, and you can apply YAML resources to create new catalog entities. These features are useful for bootstrapping clusters and promoting configuration between environments, and they are described in the official setup tools documentation (Polaris 1.8.0 setup tools) and the Polaris CLI reference.
Key limits to accept now, so you do not base an operation on a false assumption:
The examples and recommendations below assume Polaris 1.8.0 or the in-development CLI behaviors described in the Polaris CLI reference. Check those pages when you plan your exact commands and flags.
Here is a practical workflow you can adopt. I have operated similar flows in production, and I prefer explicit steps that separate reviewable artifacts from automated gates. The workflow has five stages: export a baseline, sanitize and review, commit to Git, run CI dry-run, and apply in the target cluster with a controlled rollout.
Start from a source Polaris cluster where the desired catalog resources already exist. Use the documented setup export command from the Polaris setup tools. The export will produce a YAML bundle describing the resources the tool supports.
polaris setup export --output catalog-baseline.yaml --include namespaces,projects,roles,credentialsNotes:
Commit the YAML to a reviewable branch. At this stage, remove or mask sensitive fields. I use placeholder references that CI can replace, or I keep secrets out of the repo and reference them from a secret manager during apply.
Reviewers should check resource names, namespace mappings, and any environment-specific endpoints. If you run Polaris on Kubernetes with Helm and PostgreSQL backing, confirm that the cluster configuration described in the exported YAML aligns with how your Helm chart provisions services. The Dremio guide on running Polaris in production with Kubernetes gives operational context you should reconcile with exported resources.
Before applying to an environment, validate the bundle with the setup dry-run. Run this in CI with service account credentials that have the same creation privileges as the target environment. Dry-run will perform server-side validation and report errors without persisting changes to the catalog. It is an essential gate because it catches schema and permission problems earlier than apply time.
polaris setup apply --dry-run --file catalog-baseline.yaml --target https://polaris-staging.example.comCI specifics:
Once dry-run passes in CI and reviewers approve the change, apply to the target cluster with a controlled process. Use a targeted, incremental rollout. Start with noncritical namespaces or a single project, then expand. Because apply is create-oriented and will skip existing resources, design your rollout so that new resources create cleanly and updates are handled separately.
polaris setup apply --file catalog-baseline.yaml --target https://polaris-prod.example.comVerification steps:
Polaris resources often have creation dependencies. For example, roles reference namespaces, projects reference namespaces and policies, and credentials may reference projects or identities. When you build your YAML bundle, order resources so parents come before children. The CLI will attempt to create what it can, but ordering reduces partial failures and makes logs easier to interpret. Diagram two shows a typical dependency order.
A safe, minimal ordering I use:
Polaris 1.8.0 setup documentation shows the resource kinds and what the export tool emits. Use that as the canonical list to design your ordering for your version.
Dry-run is a powerful gate in a CI pipeline, but it has limits you must test for. The dry-run performs server-side validation and simulates creation, but it will not exercise external systems that your runtime plugins call or prove that dynamic credentials will successfully be issued at runtime. Place additional integration tests that exercise those external paths behind the dry-run gate.
Specifically, dry-run will help you catch:
But dry-run will not catch:
Since apply is create-oriented, you need a drift detection strategy. Treat the setup-as-code YAML as the source of truth for desired creation state, but have an explicit reconciliation and drift feedback loop to identify differences and handle updates.
Two practical approaches I have used:
Operational caution: exported catalog files commonly contain operational detail that your team may not intend to store in Git. Treat these files as sensitive; use short-lived exports, encrypt artifacts in CI, and set tight access controls. The Polaris setup export documentation outlines which fields will appear in exports for the version you run, so check export output for sensitive values when you upgrade Polaris or change plugins.
This worked example is concrete. It assumes you have polaris installed and the network access needed for your source cluster. Confirm your installed polaris version matches the behavior described for exports in the Polaris 1.8.0 setup tools documentation or the in-development CLI docs.
# Export namespaces, projects, and roles from an existing cluster
polaris setup export --output baseline.yaml --include namespaces,projects,roles
# Inspect the first 200 lines to confirm no secrets are embedded
head -n 200 baseline.yamlSanitization example, replace credential contents with a reference key your secret manager can resolve at apply time. This is a manual step or a small script that your CI can run before committing the file.
# naive sed-based placeholder replacement for an exported credential field
sed -E 's/secret:.*/secret: REPLACE_WITH_SECRET_REF/' -i baseline.yamlAfter sanitizing, commit the file to Git and open a pull request for review.
This CI job runs the Polaris dry-run against a staging endpoint. The CI runner uses an ephemeral service account key stored in the pipeline secrets store. The job fails if polaris setup apply returns a nonzero exit status.
# Example GitLab CI job snippet
stages:
- validate
validate_polaris:
stage: validate
image: polaris/cli:1.8.0
script:
- polaris setup apply --dry-run --file baseline.yaml --target https://polaris-staging.internal
only:
- merge_requests
tags:
- ci-runnerRemember to fail the pipeline on any validation error and to archive the server response for triage. If dry-run reports failures that look like missing external services, add a separate integration job that runs against a staging environment with those services available.
After approval, apply to the production cluster in a controlled window. Start small, then expand. If your production cluster is Operating Apache Polaris with the Admin Tool: Bootstrap, Purge, and Metastore Maintenance or any other target, perform the first rollout in a sandbox or a limited-visibility namespace before a full expansion.
# Apply to production with monitoring hooks
polaris setup apply --file baseline.yaml --target https://polaris-prod.internal
# Immediately verify by listing critical resources
polaris namespace list --target https://polaris-prod.internal
polaris project list --target https://polaris-prod.internal
polaris role list --target https://polaris-prod.internalIf any creation step reports a resource already exists, the apply will typically skip it. Record such occurrences and treat them as a trigger to run an explicit reconciliation. For entries that must be updated, use the Polaris REST API with a targeted patch or update call rather than relying on setup apply.
Expect a few common failure modes. For each, I show detection and remediation steps I have used in production.
Measure both technical correctness and operational signals. I track these metrics after each rollout.
Label any facts that depended on documentation availability on November 9, 2026. The behaviors of the Polaris setup export, apply, and the CLI modes discussed above are consistent with the Polaris 1.8.0 setup tools documentation and the in-development CLI reference as of that date.
If you need architecture context for how Polaris fits into larger deployments, read the Dremio explanation of Apache Polaris architecture. For operational details on RBAC and credential vending that influence how you sanitize exports and design tests, see the Dremio post on access control in Polaris. If you plan to run Polaris on Kubernetes with Helm and PostgreSQL, the Dremio guide on running Polaris in production explains what the runtime looks like and what parts of exported configuration you should check against your Helm values.
No, not fully. Apply is create-oriented and will skip resources that already exist. It is safe to run repeatedly for creating new resources, but do not rely on it to update existing entities. Use explicit update steps for changes.
Yes, but treat exports as sensitive. Sanitize or remove secrets, encrypt artifacts in CI, and restrict repository access. Keep secret values out of the repo and reference a secret manager at apply time.
No. Dry-run validates resource shape and server-side constraints. It does not exercise external plugins or credential vending. Add integration tests that run in staging to validate runtime dependencies.
Detect skipped resources in apply logs, then run a targeted reconciliation using the Polaris REST API or scripted patches to update in-place. Avoid blind replacements from a generic apply step.
Daily exports are a good starting point. Increase frequency if you have many automated changes. Use diff thresholds to avoid noisy alerts and open tickets when meaningful differences appear.
Check the Polaris setup tools documentation for the release you run, such as the Polaris 1.8.0 setup tools guide, and consult the Polaris CLI reference for in-development or newer behaviors.
Continue with these related implementation guides.
For a deeper treatment, download Apache Polaris: The Definitive Guide, co-authored by Alex Merced and available free from Dremio.
To put the catalog and table-format ideas into practice, explore Dremio Open Catalog.
Deploy agentic analytics directly on Apache Iceberg data with no pipelines and no added overhead.
Editor’s note, September 2026. This post was published in September 2023 and some product details have changed since. Nessie is still available and self-deployable under the Apache-2.0 licence, and it remains the clearest implementation of catalog-level branching. It is not an Apache Software Foundation project, and its development has slowed considerably. For how the current […]
Iceberg keeps per-file bounds automatically but table-level NDV statistics only when you compute them. Why that decides join order, and how to keep them fresh.
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