Overview

Build a unified Iceberg REST catalog for Spark, Flink, Trino, and more with Apache Polaris as the neutral control plane for your data platform. Many data teams now run multiple engines on a shared lakehouse and discover that table formats, catalogs, and permissions are the hardest parts to standardize. Storage is scattered across object stores, schemas drift, and each engine carries its own way of handling security, credentials, and table metadata. Apache Polaris: The Complete Guide to Iceberg REST Catalog gives you a practical blueprint for solving those problems with a single catalog service. You learn how the Iceberg REST Catalog specification works, how Polaris implements it, and how to connect Spark, Flink, Trino, Starburst, Dremio, Snowflake Open Catalog, and other engines to the same governed Iceberg tables. Understand data lakehouse catalog design, Iceberg table metadata, and how REST catalogs differ from file based and metastore based approaches Learn the Iceberg REST Catalog spec in concrete terms, including endpoints, transactions, error handling, and client expectations Master Polaris concepts such as instances, catalogs, namespaces, tables, and views, and how they map to engine level objects Design robust identity and access control with realms, OIDC based authentication, RBAC roles, external policy engines such as OPA, and attribute based rules Implement secure credential vending so engines work with short lived storage credentials instead of static keys Connect Polaris to Spark, Flink, Trino, Starburst, Dremio, and Snowflake Open Catalog using REST compatible Iceberg connectors Run Polaris locally for development, then promote to Kubernetes and cloud environments using Helm, managed Postgres, TLS, ingress, and network policies Federate existing Hive metastores and other Iceberg REST catalogs behind Polaris while handling security overlaps and conflicts Set up logging, metrics, and auditing to observe catalog behavior, capture table operations, and diagnose slow queries and commit contention Apply governance patterns for domains, lifecycles, retention, soft deletion, lineage integration, discovery tools, and safe onboarding of new teams Use incident playbooks for catalog outages, partial failures, out of sync metadata, and access issues across engines Plan migrations from legacy catalogs and design future friendly architectures that coexist with systems such as Unity Catalog and Nessie This is a code heavy, configuration heavy guide with working SQL, YAML, Java and engine configuration examples that you can adapt directly into real Iceberg and Polaris deployments. Grab your copy today and give your data platform a clear, consistent Iceberg catalog strategy with Apache Polaris.

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