NOTE: I am working on a hands-on tutorial for Polaris, so please watch for the Dremio Blog in the coming days. Also, check out many other great articles on the Dremio blog about Apache Iceberg, Data Lakehouses, and more.
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Introduction
Apache Iceberg has gained popularity for transforming data lakes into data lakehouses, enabling them to serve as the central hub of modern data architecture. A key component of this transformation is using catalogs, which organize and manage data efficiently. Polaris, a new open-source solution, offers a robust and flexible way to work with Apache Iceberg catalogs. In this blog, we’ll explore Polaris, the concepts of internal and external catalogs, the various entities it manages, and its principles and security models. By the end, you’ll understand how Polaris enhances your data lakehouse experience and streamlines data management.
Resources Around the Open-Sourcing of Polaris:
Announcement Blog
Datanami Article about Merging of Polaris/Nessie
What is Polaris?
Polaris is an open-source catalog service designed to manage Apache Iceberg catalogs efficiently. It provides a robust framework for organizing, accessing, and securing data within a data lakehouse architecture. Polaris is a critical layer bridging the gap between raw data storage and advanced data processing and analytics tools.
Polaris offers several features that make it a valuable tool for data management:
Scalability: Polaris can handle large-scale data operations, making it suitable for enterprises with vast amounts of data.
Flexibility: It supports various storage types, including S3, Azure, and GCS, allowing you to choose the best storage solution for your needs.
Interoperability: Polaris integrates seamlessly with popular data processing engines like Apache Spark, Apache Flink, Snowflake and Dremio, enabling you to leverage existing tools and workflows.
Security: It implements a role-based access control (RBAC) model, ensuring that data access and management are both secure and compliant with organizational policies.
By providing a unified catalog service, Polaris simplifies the management of Iceberg tables and views, helping organizations maintain a coherent and efficient data architecture. In the following sections, we will delve deeper into the internal and external catalog concepts, entities managed by Polaris, and its principles and security model.
Entities in Polaris
Polaris manages several key entities that form the backbone of its cataloging system. These entities include catalogs, namespaces, tables, and views. Understanding these entities and their relationships is crucial for effectively utilizing Polaris in a data lakehouse architecture.
Catalogs
A catalog is Polaris’s top-level entity. It serves as a container for other entities, organizing data into a structured hierarchy. Polaris catalogs map directly to Apache Iceberg catalogs and are associated with a specific storage type, such as S3, Azure, or GCS. This association defines where the data within the catalog resides and the credentials required to access it.
Namespaces
Namespaces are logical entities within a catalog that can contain tables and views. They act as organizational units, similar to schemas or databases in traditional relational database systems. Namespaces in Polaris can be nested up to 16 levels, allowing for flexible and granular data organization. For example, a namespace structure like a.b.c.d represents a nested hierarchy where d resides within c, which is within b, and so on.
Tables
Polaris tables are entities that map to Apache Iceberg tables. They store actual data and metadata necessary for managing and querying the data efficiently. Tables in Polaris benefit from Iceberg’s capabilities, such as ACID transactions, schema evolution, and partitioning, making them highly reliable and performant for large-scale data operations.
Views
Views in Polaris are entities that map to Apache Iceberg views. They provide a way to define virtual tables based on the results of a query. Views are useful for creating reusable, queryable abstractions on top of existing tables without duplicating data. They can simplify complex queries and enhance data accessibility for different user groups.
By organizing data into these entities, Polaris ensures that your data lakehouse is well-structured, easily navigable, and ready for advanced data processing and analytics tasks. In the next section, we will explore the concepts of internal and external catalogs within Polaris.
Catalogs in Polaris
Polaris introduces the concepts of internal and external catalogs to provide flexibility and control over how data is organized and accessed within a data lakehouse architecture.
Internal Catalogs
Internal catalogs are managed entirely by Polaris. They are self-contained within the Polaris system, meaning that all metadata and data management operations are handled by Polaris itself. This approach simplifies the setup and management process, as users can rely on Polaris to maintain consistency, security, and performance.
Internal catalogs are ideal for scenarios where centralized control over data management is desired. They ensure that all data governance policies, access controls, and data lineage tracking are enforced consistently. By using internal catalogs, organizations can benefit from a streamlined data management experience without needing to configure and manage external systems.
External Catalogs
External catalogs, on the other hand, integrate with existing catalog services outside of Polaris. They allow Polaris to interact with and manage data that resides in external systems. This integration enables organizations to leverage Polaris’s features while still utilizing their current data infrastructure.
External catalogs are useful for organizations that have existing investments in other catalog systems and want to incorporate Polaris’s capabilities without disrupting their current workflows. By configuring Polaris to work with external catalogs, users can extend Polaris’s benefits, such as enhanced security and interoperability, to their existing data assets.
Comparison and Use Cases
Internal Catalogs: Best suited for organizations looking for a unified and centralized data management solution. They offer simplicity and comprehensive control over data governance.
External Catalogs: Ideal for organizations with established catalog systems that want to enhance their data management capabilities without a complete overhaul. They provide flexibility and integration with existing infrastructures.
The distinction between internal and external catalogs in Polaris offers organizations the flexibility to choose the best approach.
Principles in Polaris
Polaris employs a robust security and governance framework centered around the concept of principles. This framework ensures that data access and management are secure and compliant with organizational policies. The key components of this framework are principal roles, catalog roles, and privileges, all managed through a role-based access control (RBAC) model.
Principal Roles
Principal roles logically group service principals (users or services) together, making it easier to manage permissions and access controls. Each principal role can be assigned to multiple service principals, allowing consistent access policy application across different users and services. For example, a DataEngineer principal role might be assigned to all users performing data engineering tasks, granting them the necessary privileges to manage tables and execute queries.
Catalog Roles
Catalog roles define a set of permissions for actions that can be performed on a catalog and its entities, such as namespaces and tables. These roles are specific to a particular catalog and can be assigned to one or more principal roles. For instance, a CatalogAdministrator role might be granted full access to create, modify, and delete tables within a specific catalog. In contrast, a CatalogReader role might be limited to read-only access.
Privileges
Privileges are the specific actions that can be performed on securable objects within Polaris. These actions include creating, reading, updating, and deleting catalogs, namespaces, tables, and views. Privileges are granted to catalog roles, which are then assigned to principal roles, ensuring a hierarchical and controlled distribution of access rights. Some common privileges include:
CATALOG_MANAGE_ACCESS: Grants the ability to manage access permissions for a catalog.
CATALOG_MANAGE_CONTENT: Allows full management of a catalog’s content, including metadata and data operations.
TABLE_READ_DATA: Permits reading data from a table.
TABLE_WRITE_DATA: Allows writing data to a table.
Role-Based Access Control (RBAC)
The RBAC model in Polaris ensures access is granted based on roles rather than directly to individual users or services. This model simplifies permissions management and ensures access policies are consistently enforced. By defining roles and assigning them to principals, Polaris allows for scalable and maintainable access control.
For example, in a typical scenario, an organization might have the following setup:
Principal Role: DataEngineer: Assigned to users who manage data processing tasks.
Catalog Role: TableManager: Grants privileges to create, read, update, and delete tables.
Privileges: Specific actions like TABLE_CREATE, TABLE_READ_DATA, and TABLE_WRITE_DATA are granted to the TableManager role.
By assigning the TableManager catalog role to the DataEngineer principal role, all data engineers gain the necessary permissions to perform their tasks on the specified catalogs.
Polaris’s principles and RBAC model provide a secure and efficient way to manage access and permissions within your data lakehouse architecture. By leveraging these concepts, organizations can ensure that their data is both accessible to those who need it and protected from unauthorized access.
Conclusion
Polaris offers a comprehensive and flexible solution for managing Apache Iceberg catalogs within a data lakehouse architecture. By robustly supporting internal and external catalogs, Polaris ensures that organizations can choose the best approach for their existing infrastructure and data management needs.
The various entities managed by Polaris, including catalogs, namespaces, tables, and views, are designed to provide a structured and efficient way to organize and access data. Polaris’s role-based access control (RBAC) model further enhances security and governance, ensuring that data is accessed and managed in compliance with organizational policies.
Polaris’s Iceberg REST Catalog integration enables working with popular data processing engines like Apache Spark, Snowflake, and Dremio and its support for various storage types make it a versatile tool for modern data architectures.
Polaris simplifies the management of Apache Iceberg catalogs and enhances the overall data lakehouse experience by providing a unified, secure, and efficient framework for data management. Whether you want to streamline your data governance or integrate with existing systems, Polaris offers the tools and capabilities to meet your needs.
GET HANDS-ON WITH APACHE ICEBERG
Below are list of exercises to help you get hands-on with Apache Iceberg to see all of this in action yourself!
Intro to Apache Iceberg, Nessie and Dremio on your Laptop
JSON/CSV/Parquet to Apache Iceberg to BI Dashboard
From MongoDB to Apache Iceberg to BI Dashboard
From SQLServer to Apache Iceberg to BI Dashboard
From Postgres to Apache Iceberg to BI Dashboard
Mongo/Postgres to Apache Iceberg to BI Dashboard using Git for Data and DBT
Elasticsearch to Apache Iceberg to BI Dashboard
MySQL to Apache Iceberg to BI Dashboard
Apache Kafka to Apache Iceberg to Dremio
Apache Iceberg Lakehouse Engineering Video Playlist
Apache Druid to Apache Iceberg to BI Dashboard
Postgres to Apache Iceberg to Dashboard with Spark & Dremio
