Apache Polaris: How Policy-Managed Table Maintenance Eliminates Iceberg Operational Overhead

In our previous post, we covered how to control Iceberg file sizes at write time and how to fix small file problems with Iceberg's table maintenance procedures. The conclusion was clear: the tools are excellent, but manually scheduling and managing maintenance across dozens or hundreds of tables does not scale.

This post is about the layer that solves that problem: Apache Polaris — the open-source Iceberg catalog that introduces policy-based table maintenance, letting you define optimization rules once and have them applied automatically across your entire lakehouse.

What Is Apache Polaris?

Apache Polaris is an open-source, vendor-neutral catalog for Apache Iceberg. Originally developed at Snowflake and donated to the Apache Software Foundation, Polaris implements the Iceberg REST Catalog specification — the standard API that any Iceberg-compatible engine can use to discover and manage tables.

But Polaris is more than just a catalog that stores table metadata. It brings three capabilities that set it apart:

1. Multi-Engine Interoperability

Because Polaris implements the Iceberg REST Catalog API, any engine that supports the REST catalog protocol can use it — Spark, Flink, Trino, Dremio, Snowflake, StarRocks, and more. You register your tables once in Polaris, and every engine in your ecosystem can discover and query them through a single catalog endpoint.

This is fundamentally different from catalogs that are tightly coupled to a specific engine or vendor. With Polaris, your catalog is neutral ground — it belongs to your data platform, not to any single compute engine.

2. Credential Vending

Polaris handles secure credential management for accessing your cloud storage. Instead of embedding S3 credentials in every engine's configuration, Polaris vends short-lived, scoped credentials to engines at query time. This centralizes access control and eliminates credential sprawl across your data platform.

3. Policy-Based Table Maintenance

This is the capability we will focus on for the rest of this post. Polaris lets you define maintenance policies — declarative rules for how tables should be optimized — and attach them at the catalog, namespace, or individual table level. The policies flow downward through inheritance, so a single policy defined at the catalog level can govern maintenance for every table in your lakehouse.

The Problem Polaris Policies Solve

Let us revisit the operational reality from the previous post. A typical mid-size data platform has:

• 50-200 Iceberg tables
• Multiple write patterns (batch ETL, micro-batch streaming, CDC)
• Different compaction needs per table (binpack for streaming tables, sort for analytical tables)
• Different retention requirements (7-day snapshots for operational tables, 90-day for regulatory tables)
• Different teams writing to different tables with different schedules

Managing this manually means:

• Custom Airflow DAGs (or equivalent) for each table's maintenance schedule
• Per-table configuration for compaction strategy, target file size, and retention
• Monitoring and alerting for failed maintenance jobs
• Cross-team coordination to avoid running maintenance during peak write windows

What starts as a simple cron job inevitably becomes a maintenance-of-the-maintenance problem. The platform team spends more time managing table optimization than building data products.

Polaris policies replace all of this with a declarative model: tell the catalog what you want, and let the catalog figure out when and how to do it.

How Polaris Policies Work

Policy Types

Polaris supports system-defined policy types for the most common maintenance operations:

system.data-compaction — Controls how and when data files are compacted. You can specify the compaction strategy (binpack, sort, z-order), target file size, minimum file count thresholds, and maximum concurrent file group rewrites.

system.snapshot-expiry — Controls when old snapshots are removed. You define retention periods and minimum snapshot counts to keep, balancing storage costs against time travel requirements.

system.metadata-compaction — Controls the optimization of metadata files (manifests, manifest lists). This keeps query planning fast as tables accumulate write history.

system.orphan-file-removal — Controls the cleanup of data files that are no longer referenced by any table metadata. Safety retention periods prevent deletion of in-progress writes.

Creating Policies

Policies are created through the Polaris REST API with a name, type, and configuration content:

# Create a data compaction policy
curl -X POST "https://polaris-host/api/v1/catalogs/production/policies" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "standard-compaction",
    "type": "system.data-compaction",
    "description": "Standard compaction policy for batch tables",
    "content": {
      "target_file_size_bytes": 268435456,
      "compaction_strategy": "bin-pack",
      "min_input_files": 5,
      "max_concurrent_file_group_rewrites": 10
    }
  }'

# Create a snapshot expiry policy
curl -X POST "https://polaris-host/api/v1/catalogs/production/policies" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "standard-retention",
    "type": "system.snapshot-expiry",
    "description": "7-day snapshot retention for operational tables",
    "content": {
      "max_snapshot_age_ms": 604800000,
      "min_snapshots_to_keep": 5
    }
  }'

Policy Attachment and Inheritance

This is where Polaris policies become truly powerful. Policies can be attached at three levels:

Catalog (top-level)
  └── Namespace (schema/database)
        └── Table (individual table)

Inheritance flows downward. A compaction policy attached at the catalog level applies to every table in every namespace — unless overridden by a more specific policy at the namespace or table level.

# Attach compaction policy at catalog level (applies to all tables)
curl -X PUT "https://polaris-host/api/v1/catalogs/production/policies/standard-compaction/attach" \
  -H "Content-Type: application/json" \
  -d '{
    "target": {
      "type": "catalog",
      "name": "production"
    }
  }'

# Override with a different policy for a specific namespace
curl -X PUT "https://polaris-host/api/v1/catalogs/production/policies/aggressive-compaction/attach" \
  -H "Content-Type: application/json" \
  -d '{
    "target": {
      "type": "namespace",
      "name": "streaming_events"
    }
  }'

# Override for a single high-traffic table
curl -X PUT "https://polaris-host/api/v1/catalogs/production/policies/custom-compaction/attach" \
  -H "Content-Type: application/json" \
  -d '{
    "target": {
      "type": "table",
      "namespace": "streaming_events",
      "name": "clickstream"
    }
  }'

This hierarchical model lets you define sensible defaults at the top and exceptions where needed — without managing individual configurations for every table.

Querying Applicable Policies

You can query which policies apply to any entity in your catalog:

# What policies apply to a specific table?
curl "https://polaris-host/api/v1/catalogs/production/namespaces/analytics/tables/orders/applicable-policies"

This returns the effective policy for the table, including any inherited policies and overrides. It makes it straightforward to audit your maintenance configuration across the entire catalog.

A Real-World Policy Architecture

Here is how a well-structured policy hierarchy might look for a production lakehouse:

Catalog-Level Defaults

Policy: "default-compaction"
  Type: system.data-compaction
  Strategy: bin-pack
  Target file size: 256 MB
  Min input files: 5
  Applied to: Catalog (production)

Policy: "default-retention"
  Type: system.snapshot-expiry
  Max age: 7 days
  Min snapshots: 5
  Applied to: Catalog (production)

Policy: "default-orphan-cleanup"
  Type: system.orphan-file-removal
  Retention: 3 days
  Applied to: Catalog (production)

Every table in your catalog automatically gets these defaults. No per-table configuration needed.

Namespace Overrides

Namespace: "streaming_events"
  Policy: "streaming-compaction"
    Type: system.data-compaction
    Strategy: bin-pack
    Target file size: 128 MB
    Min input files: 3
    → Overrides catalog default for all streaming tables

Namespace: "regulatory_reporting"
  Policy: "regulatory-retention"
    Type: system.snapshot-expiry
    Max age: 90 days
    Min snapshots: 30
    → Overrides catalog default for compliance tables

Streaming tables get more aggressive compaction (smaller target, lower threshold) because they produce small files more frequently. Regulatory tables keep 90 days of snapshots for audit compliance.

Table-Level Exceptions

Table: "streaming_events.clickstream"
  Policy: "high-throughput-compaction"
    Type: system.data-compaction
    Strategy: bin-pack
    Target file size: 128 MB
    Min input files: 2
    Max concurrent rewrites: 20
    → Highest-traffic table gets the most aggressive compaction

The entire maintenance strategy is captured in a handful of policy definitions. No Airflow DAGs. No cron jobs. No per-table scripts.

How Cazpian's Managed Polaris Catalog Works

Cazpian provides a fully managed Apache Polaris catalog as part of the platform. This means you get all the policy-based maintenance capabilities of Polaris without the operational overhead of deploying, scaling, and monitoring a catalog server.

What "Managed" Means

When you create a catalog in Cazpian, the platform provisions a Polaris instance that:

• Runs in your AWS account — your metadata stays in your VPC, alongside your data
• Scales automatically — the catalog handles concurrent requests from multiple Spark clusters, Trino engines, and BI tools without manual capacity planning
• Integrates with Cazpian's compute — Spark jobs automatically discover tables through the Polaris REST endpoint. No manual catalog configuration per job.

Policy-Managed Table Maintenance in Cazpian

Cazpian's managed Polaris catalog goes beyond storing policies — it executes them. The platform continuously monitors your Iceberg tables and automatically triggers maintenance operations when policies indicate action is needed.

Here is what happens behind the scenes:

1. Continuous table analysis — Cazpian monitors table metrics: file count, file sizes, snapshot age, manifest count, and orphan file accumulation
2. Policy evaluation — When metrics cross the thresholds defined in your policies (e.g., a table has more than 5 files below the minimum size), the platform flags the table for maintenance
3. Automatic execution — Cazpian schedules and runs the appropriate maintenance procedure (compaction, snapshot expiry, orphan cleanup, or manifest rewrite) using managed Spark compute
4. Smart scheduling — Maintenance runs during low-utilization windows to avoid competing with your production workloads for compute resources

What You Configure vs. What Cazpian Handles

Responsibility	You	Cazpian
Define compaction strategy per workload	Set policy	Execute compaction
Set snapshot retention periods	Set policy	Expire snapshots on schedule
Handle orphan file cleanup	Set policy (or use defaults)	Detect and remove orphans safely
Optimize metadata	Set policy (or use defaults)	Rewrite manifests automatically
Monitor maintenance health	Review dashboard	Detect failures, retry, alert
Scale maintenance compute	Nothing	Allocate right-sized resources
Coordinate with production workloads	Nothing	Schedule during low-utilization

You define the what (policies). Cazpian handles the when, how, and how much compute to use.

Getting Started

Setting up policy-managed maintenance in Cazpian takes three steps:

Step 1: Create your catalog

When you provision a Cazpian workspace, a managed Polaris catalog is created automatically. Your Spark jobs connect to it via the REST catalog endpoint — no additional configuration needed.

Step 2: Define your policies

Use the Cazpian console or the Polaris REST API to create policies that match your workload patterns. Start with the defaults and override where needed.

Step 3: Attach policies

Attach policies at the catalog level for global defaults, then override at the namespace or table level for workloads with special requirements. Cazpian begins monitoring and maintaining your tables immediately.

Why Policy-Based Maintenance Matters

The shift from manual maintenance to policy-based maintenance is not just about convenience — it changes the economics and reliability of running an Iceberg lakehouse.

Consistency: Every table gets maintained according to its policy. No table falls through the cracks because someone forgot to add it to the Airflow DAG.

Scalability: Adding 50 new tables does not mean writing 50 new maintenance jobs. They inherit the catalog-level policy automatically.

Auditability: The policy hierarchy is a single source of truth for your maintenance configuration. You can audit what applies where with a single API call.

Separation of concerns: Data engineers build pipelines. Platform teams define policies. Neither needs to manage the other's operational details.

Cost efficiency: Automated maintenance prevents small file accumulation from compounding into expensive query degradation. Fixing a small problem early is always cheaper than fixing a large problem late.

Polaris in the Catalog Landscape

Apache Polaris is not the only Iceberg catalog option. Here is how it compares:

Capability	Apache Polaris	Project Nessie	Unity Catalog
Governance model	Apache Software Foundation	Community-driven (Dremio)	Linux Foundation (Databricks)
REST Catalog API	Native implementation	Supported	Supported
Policy-based maintenance	Built-in (system policies)	Not built-in	Not built-in (separate service)
Git-like versioning	Not supported	Core feature (branches, tags, commits)	Not supported
Multi-format support	Iceberg-focused	Iceberg-focused	Delta Lake, Iceberg, Hudi
Credential vending	Built-in	Via configuration	Built-in
Vendor neutrality	Fully vendor-neutral	Dremio-associated	Databricks-associated

When to choose Polaris: You want a vendor-neutral, Iceberg-native catalog with built-in policy management and you do not need Git-style data versioning.

When to choose Nessie: You need Git-like branching and tagging for your data — useful for testing schema changes or running experiments on isolated branches of your data.

When to choose Unity Catalog: You are in the Databricks ecosystem and need multi-format support (Delta + Iceberg + Hudi) with integrated governance.

From Manual to Automatic: The Maturity Curve

Most data teams follow a predictable journey with Iceberg table maintenance:

Level 0 — Ignore it. Tables degrade over time. Query performance issues are treated as mysteries.

Level 1 — Ad-hoc scripts. Someone writes a Spark job to compact the worst tables. It runs when someone remembers to trigger it.

Level 2 — Scheduled maintenance. Airflow DAGs or cron jobs run maintenance procedures on a fixed schedule. Better, but brittle and hard to maintain as the table count grows.

Level 3 — Policy-managed maintenance. Policies define the desired state. The catalog (and platform) ensure tables converge toward that state automatically. This is where you want to be.

Cazpian's managed Polaris catalog takes you directly to Level 3 — without building the infrastructure that Levels 1 and 2 require as stepping stones.

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Ready to stop babysitting your Iceberg tables? Cazpian provides a fully managed Apache Polaris catalog with policy-based table maintenance, zero cold-start Spark compute, and usage-based pricing — all running in your AWS account. Learn more about our architecture.