Polaris Catalog + Atlan: Better Together

Updated July 30th, 2024

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As a Polaris Catalog launch partner, Atlan will be one of the first data and AI governance platforms to integrate with Polaris Catalog. This integration will extend Atlan’s leading data and AI governance functionalities to Iceberg tables, allowing customers to find, trust, and govern Iceberg tables along with the rest of the data and AI landscape.

Enterprise customers are increasingly looking for openness & interoperability when building their data and AI stack, with the ability to choose best-in-class solutions without getting locked in to any single vendor. Atlan’s open and extensible platform is fundamentally designed for this kind of interoperability — seen in the integration for OpenLineage, an open standard for data lineage, which ingests real-time pipeline metadata and creates automated lineage for Airflow and Spark.

By extending support for Iceberg tables through Polaris Catalog, Atlan will give customers the ability to make trusted decisions with confident data, assure data security & compliance, accelerate data and AI innovation, and improve the ROI on their data and AI investment.

Table of contents #

  1. Why use Polaris Catalog and Atlan together?
  2. Atlan + Polaris Catalog: A unified layer for open data and AI governance
  3. Polaris Catalog + Atlan = Better together
  4. Polaris Catalog + Atlan: Related reads

Why use Polaris Catalog and Atlan together? #

Modern data environments are diverse and multi-cloud, catering to diverse teams, tools, and use cases. Meanwhile, metadata is growing in significance, as high-quality metadata is central to powering AI and LLM applications.

That’s why enterprises are looking for an active metadata platform that acts as a unified control plane built for diversity:

  • Diversity in data assets to include databases, schemas, dashboards, requests, pipelines, READMEs, notebooks, code snippets, metrics, Iceberg tables, and more.
  • Diversity in humans of data such as, analysts, engineers, data scientists, business analysts, and financial analysts.
  • Diversity in data tools which include metadata lakes, cloud data warehouses, ETL tools, BI platforms, data processing frameworks, orchestration engines, communication tools, and technical catalogs like Polaris Catalog.
  • Diversity in use cases which may vary depending on the industry and the organization’s data maturity.

Polaris Catalog is an open-source, technical catalog for Iceberg-specific use cases, helping expose metadata about data assets, products, lineage, tags, policies, and more. Using Polaris, engineers, developers, and architects can use multiple engines (Apache Flink, Apache Spark, PyIceberg, Snowflake, Trino) to read and write Iceberg tables into Polaris. Since Polaris is independent from Snowflake, it’s interoperable across engines such as Apache Flink, Apache Spark, Dremio, Python, and Trino — without any vendor lock-in for Iceberg architecture.

Read more → Everything you need to know about Polaris Catalog

Meanwhile, Atlan is a metadata control plane, i.e., a comprehensive, unified layer that connects with every tool in the data stack. Polaris Catalog makes it easier to ingest Iceberg metadata into Atlan, making Atlan the unified control plane that acts as a single pane of glass to consume and curate all kinds of metadata, enabling collaboration and governance for data and AI initiatives across teams.

Atlan + Polaris Catalog: A unified layer for open data and AI governance #

Atlan’s integration with Polaris Catalog will give customers using Iceberg tables a unified control plane to access and manage metadata across the enterprise, enabling them to deliver:

  • Trusted data for confident decisions
  • Assured data security and regulatory compliance
  • Accelerated data and AI innovation
  • ROI on data and AI investment

Let’s look at some examples of how Atlan and Polaris Catalog can deliver each of these benefits.

Trusted data for confident decisions #


Polaris empowers developers, engineers, and architects to seamlessly work with data wherever it resides by federating disparate catalogs and enabling queries across cloud storage systems.

Integrating Polaris Catalog with Atlan also brings:

  • A business glossary that brings context where users work. For instance, a Chrome extension in BI dashboards.

Business glossary in Atlan

Business glossary in Atlan - Image by Atlan.

  • Data product marketplace with a complete repository of trusted data products, each owned and maintained by their domains.

A data product marketplace curated to domains

A data product marketplace curated to domains - Image by Atlan.

  • Proactive impact analysis and issue alerting powered by cross-system, column-level actionable data lineage for a single pane of glass.

Cross-system, column-level, actionable lineage

Cross-system, column-level, actionable lineage - Image by Atlan.

Assured data security and regulatory compliance #


Polaris Catalog is complemented by Snowflake Horizon’s enterprise-grade security. Atlan enhances Snowflake Horizon with:

  • Auto-propagation of data classification with bi-directional tag sync for Snowflake assets.

Auto-propagation of data tags and classification via lineage

Auto-propagation of data tags and classification via lineage - Image by Atlan.

Policy Center in Atlan offering real-time view of policy coverage, incidents, and more

Policy Center in Atlan offering real-time view of policy coverage, incidents, and more - Image by Atlan.

  • A no-code setup for data stewards to configure custom workflows.

No-code policy setting, enforcement, and execution in Atlan

No-code policy setting, enforcement, and execution in Atlan - Image by Atlan.

  • Data contracts that embed data governance guardrails into the data producer tools and workflows.
  • Governance by exception — alerting data stewards whenever a data asset doesn’t comply with data governance policies.

Governance by exception in action

Governance by exception in action - Image by Atlan.

Accelerated data and AI innovation #


Combining Polaris Catalog and Atlan can improve data search, discoverability, and context across the data estate with:

  • An intuitive UX that powers natural language search across the data universe — Tableau dashboards, dbt models, Airflow DAGs, etc.

Natural language search with custom metadata filters

Natural language search with custom metadata filters - Image by Atlan.

  • Intelligent automation that scales data asset documentation, tag propagation, PII data classification, asset recommendations, and more.

Intelligent automation to save time, cut costs, support compliance, and drive productivity in Atlan

Intelligent automation to save time, cut costs, support compliance, and drive productivity in Atlan - Image by Atlan.

ROI on data and AI investment #


Atlan supports open APIs to bring in metadata from any tool in the data stack — for e.g. runtime metrics from data processing engines, or usage metrics from BI tools.

Atlan’s real-time automated metadata capture layer is configurable, extensible, and provides an overall view of data estate and usage.

This helps in optimizing the ROI of data and AI investments by enabling data teams to:

  • Track the popularity of each data asset with usage metadata to spot and review underutilized assets.
  • Analyze popularity metrics to allocate resources better and optimize investments in tools and technologies.
  • Discover related data assets and decrease the time-to-insight, while driving decision-making.

Polaris Catalog + Atlan = Better together #

By combining the interoperability of Polaris Catalog with Atlan’s metadata control plane, customers can implement open, active data and AI governance across their data ecosystem — building trust in data, assuring security & compliance, and accelerating their data and AI initiatives.

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