Apache Iceberg Tables Data Governance: Here Are Your Options in 2025

Updated March 04th, 2025

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Apache Iceberg provides the framework for metadata management of data assets in table formats with support for ACID transactions, time-travel for point-in-time queries, and version control.

Regarding Apache Iceberg tables governance, Iceberg doesn’t go beyond providing a few table-level properties for encryption. The governance and security of Iceberg tables are usually handled by other tools in the computing, governance, quality, and storage layers that work with it.
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This article will take you through some of the key Apache Iceberg table governance features and explain the role of Snowflake’s Polaris, an open-source Iceberg catalog. You’ll also see how integrating Polaris with Atlan can further improve metadata management, lineage tracking, policy enforcement, and overall governance for data lakes and lakehouses.

Table of Contents #

  1. Data governance and security for Iceberg tables
  2. Apache Iceberg tables governance with Polaris: How does it work?
  3. Atlan + Polaris: Data and AI governance for Apache Iceberg tables and more
  4. Apache Iceberg tables governance: Bottom line
  5. Apache Iceberg Tables Governance: Related reads

Data governance and security for Iceberg tables #

Before we begin, it is important to reiterate that the core function of Apache Iceberg is to provide a table format specification so that the data can be more efficiently organized, accessed, and updated at scale.

Apache Iceberg has a built-in cloud-agnostic tamper-proof encryption mechanism, which you can use to encrypt your files. You can encrypt and decrypt the data with a master key that would give you access to all the metadata and data that comprise a given table.

Other security and governance measures, such as applying the principle of least privilege for access, enforcing multi-factor authentication, using access control lists, and extensive logging and monitoring, are handled by other tools that integrate with Iceberg. Some of these tools can work on the data/storage layer, while others work on the metadata layer.

The data layer security and governance can be handled by the cloud platform where your data lake or lakehouse is hosted, but more granular data governance happens at the metadata layer.

The following architecture diagram depicts the separation of the data and metadata layers and the catalog maintaining the metadata for any given Iceberg table.

The data and metadata layers in Iceberg

The data and metadata layers in Iceberg - Source: Apache Iceberg documentation.

A tool like Project Nessie, a catalog for Apache Iceberg, provides Git-like version control for data and also has authorization features like access control. However, the access control is limited to the metadata layer, i.e., it can only protect the metadata, not the data.

Tabular offers another implementation of the Iceberg specification wrapped with a host of features, one of which is the Tabular RBAC model. The same is true for AWS Glue Data Catalog combined with Lake Formation’s Tag-based Access Control model.

Polaris, which was initially developed by Snowflake and later donated to the Apache Software Foundation, where it is currently hosted as an incubating project, is also an option.

Polaris is a unique catalog implementation for Apache Iceberg. It achieves feature parity with the original catalog specification and provides advanced features like Role-based access control and credential vending.

Read more → Everything you need to know about Snowflake Polaris

Some catalogs like AWS Glue Data Catalog don’t match the extent and maturity of Iceberg support in Polaris. In contrast, others are used for specific purposes, such as having a Git-like version control for data objects with Project Nessie. For native Iceberg support, Polaris is much better than these alternatives.

In the next section, let’s look at some of the governance features of Apache Polaris and see how they work.

Apache Iceberg tables governance with Polaris: How does it work? #

Polaris is the first fully-featured implementation of the Iceberg REST catalog. It aims to establish a centralized interface for reading and writing Iceberg tables and provide seamless multi-engine interoperability, including Apache Spark, Apache Flink, Dremio, and Trino.

In June 2024, Snowflake announced the creation of Polaris, an open-source catalog for Iceberg. A month later, the project was open-sourced. The team that was responsible for creating Apache Arrow and Project Nessie–significant contributors to Apache Iceberg–are also contributing to Polaris to bring Project Nessie’s features to it.

One of the key goals of creating Polaris was to increase interoperability between cloud platforms, storage engines, query engines, and data catalogs. Here’s a diagram depicting where Polaris sits in a modern data stack.

How Polaris fits into your data stack

How Polaris fits into your data stack - Source: Snowflake blog.

In addition to implementing the Apache Iceberg REST API with all its features, Polaris also implements the following:

  • Role-based access control
  • Credential vending between the storage layer and the catalog layer
  • Logging and observability

Let’s see how.

Role-based access control (RBAC) #


Every securable object–a catalog, a namespace, an Iceberg table, or a view–in your data lake or lakehouse can be secured and protected by the role-based access control (RBAC) framework.

The principal role is granted to Polaris service principals or its equivalents in your organization. Catalog roles, on the other hand, can be configured with specific privileges on resources and granted to the principal roles.

Credential vending #


Polaris also handles credential vending where it vends temporary storage credentials to the query engine while the queries are getting executed. The query engine requires this to run a query without needing access to the cloud storage to access Iceberg tables.

Logging and observability #


For logging and observability, Polaris publishes metrics using Micrometer and traces using the OpenTelemetry protocol. Polaris uses Quarkus for logging.

Plenty of other features are currently in development, some related to enhancements in the data security and governance space. One example is the implementation of row and column level access control, which might be developed soon.

While the features in Polaris get built, you can use Polaris as the foundational catalog in conjunction with the metadata platform Atlan. This way, you’ll get all your Iceberg data assets in Polaris and leverage the wide array of advanced data governance features that Atlan has to offer.

Atlan + Polaris: Data and AI governance for Apache Iceberg tables and more #

Integrating Atlan and Polaris brings the best of both worlds to your organization’s data cataloging and governance capabilities. This integration allows you to get an Iceberg REST catalog to Atlan’s metadata control plane, empowering your entire organization to discover and consume these data assets with a consistent framework and user experience.

Atlan brings various features that build upon the Polaris catalog, some of which are listed below:

These are some key examples of the capabilities that Atlan brings to the table. Atlan’s integration with Polaris will continue to develop as the Polaris project moves toward maturity and acceptance. In the meantime, watch for updates on the official website of Apache Polaris.

Apache Iceberg tables governance: Bottom line #

In this article, we explored some of the key features of Apache Iceberg, such as ACID transactions, time travel, version control, and its built-in governance capabilities, which are currently limited to table-level encryption.

Other tools are required for any other advanced governance capabilities. Polaris is one such tool that implements an RBAC framework on top of a catalog built for Apache Iceberg using the REST API. However, this isn’t enough for users that need advanced governance features, such as data lineage, business cataloging, and centralized policy management and enforcement. That’s where Atlan’s integration with Polaris came in, offering you the ideal data governance solution for your data lakes and lakehouses.

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