Matt Faltyn

Open-source engineering / Apache Polaris

Making Apache Polaris Configuration Automation Reliable

I contributed seven merged fixes to Apache Polaris’s Python CLI to detect failures and preserve configuration across export and reapply.

My role
Upstream contributor
Surface
Python / Setup CLI
Delivered
7 merged PRs
Merged
July 24 – August 17, 2026

Why this matters

Apache Polaris helps data engines find and manage Apache Iceberg tables. Its setup CLI exports configuration to YAML and reapplies it to an environment.

That process could report success after failed operations or silently lose configuration. I fixed failure reporting, missing objects, and altered content while preserving existing input formats.

About Apache Polaris

The workflow

  1. Catalog configuration
  2. Export
  3. YAML file
  4. Apply
  5. Recreated configuration
Preserve supported configuration from export to reapply, and make incomplete operations detectable by automation.

What changed

Detect incomplete operations

Preserve configuration structure

Preserve content and metadata

How I validated the fixes

I reused existing traversal and failure-tracking code, kept older configuration formats working, and updated the setup documentation.

The PR records document regression tests for export → YAML → apply, failed API calls, nested resources, duplicate names, and mixed input formats, plus unit, lint, and applicable build or integration checks. These are the upstream validation results.

The scope is supported setup configuration, not complete disaster recovery. Apply remains best effort; policy attachments are not exported.

Let’s talk data platforms

Discuss data platform engineering