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Dremio Blog: Open Data Insights
Apache Iceberg 1.12.0: What’s New, Breaking Changes, and Upgrade Guide
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Dremio Blog: Open Data Insights
Apache Polaris 1.8.0: What’s New, Breaking Changes, and Upgrade Guide
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Dremio Blog: Open Data InsightsApache Iceberg Table Encryption: KMS, Envelopes, and Parquet Encryption
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Dremio Blog: Open Data InsightsApache Iceberg Views: Portable View Metadata Across SQL Engines
Browse All Blog Articles
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Dremio Blog: Open Data Insights
Apache Iceberg 1.12.0: What’s New, Breaking Changes, and Upgrade Guide
Apache Iceberg 1.12.0 expands v3 engine support, REST catalog operations, streaming reliability, and v4 groundwork. Learn how to upgrade safely. -
Dremio Blog: Open Data Insights
Apache Polaris 1.8.0: What’s New, Breaking Changes, and Upgrade Guide
Apache Polaris 1.8.0 adds semantic-model privileges, safer commit handling, pagination controls, CLI improvements, and important database upgrade steps. -
Dremio Blog: Open Data Insights
Apache Iceberg Geospatial Types: Geometry and Geography in the Lakehouse
Apache Iceberg v3 defines geometry and geography as first-class primitive types. Values are encoded as WKB in Avro and Parquet, with optional CRS metadata and spatial bounds that engines can use for pruning. The specification is stable, but engine support is version-specific, so teams should test every reader and writer before upgrading a shared table. -
Dremio Blog: Open Data Insights
Apache Iceberg Views: Portable View Metadata Across SQL Engines
The Apache Iceberg View Spec makes view metadata portable by storing versioned schemas, catalog and namespace context, dependencies, properties, and one or more SQL representations. It does not make every SQL expression universally executable. Portability depends on a catalog and engines that implement the spec and agree on a supported dialect representation. -
Dremio Blog: Open Data Insights
Apache Iceberg Table Encryption: KMS, Envelopes, and Parquet Encryption
Iceberg table encryption separates file encryption from key management. Writers create data keys, encrypt files, wrap those keys with a key encryption key in a KMS, and store key metadata with the files. Iceberg can encrypt data files, delete files, manifests, and manifest lists. Table metadata JSON is intentionally not encrypted by the Iceberg encryption specification. -
Dremio Blog: Open Data Insights
Server-Side Scan Planning in the Iceberg REST Catalog Explained
Server-side scan planning lets an Iceberg REST catalog plan table scans close to metadata instead of sending every manifest to the client. A client discovers the capability, submits a scan request, receives completed tasks or an asynchronous plan identifier, then polls for batches and can cancel work it no longer needs. The feature reduces metadata transfer, but it moves planning load and observability into the catalog service. -
Dremio Blog: Open Data Insights
Multi-Table Transactions with Iceberg REST Catalogs and Apache Polaris
The Iceberg REST protocol defines an optional transaction commit endpoint that can submit updates for several tables as one catalog operation. That endpoint can provide catalog-level atomicity when the server implements it, but a usable multi-table transaction also requires client support, conflict handling, and clear failure semantics. A REST catalog label alone does not guarantee that an engine will issue coordinated commits. -
Dremio Blog: Open Data Insights
MERGE INTO Patterns for Apache Iceberg: Upserts and SCD Type 2
MERGE INTO is the clearest way to express keyed changes against an Iceberg table, but reliable upserts start before the MERGE. Deduplicate the source to one action per key, define late-arrival rules, and make the batch replayable. For SCD Type 2, close the current row and insert a new version in a controlled transaction, then compact the delete and data files the workload creates. -
Dremio Blog: Open Data Insights
Apache Iceberg Storage Costs: Requests, Retention, and Lifecycle Rules
Iceberg storage cost is not only the number of terabytes in data files. It includes PUT, GET, LIST, and DELETE requests, retained snapshots, delete files, manifests, metadata JSON, compaction rewrites, and recovery windows. The safest cost plan starts with table-aware expiration and orphan cleanup, then applies object-store lifecycle rules only to paths and objects whose ownership is understood. -
Dremio Blog: Open Data Insights
Monitoring Iceberg Table Health with Metrics Reports and Metadata Tables
Healthy Iceberg monitoring combines event reports with table-state queries. ScanReport shows planning and filtering behavior. CommitReport records write outcomes and snapshot changes. Metadata tables reveal file sizes, delete-file pressure, snapshot growth, and manifest shape. Together they support SLOs for freshness, commit reliability, planning latency, and maintenance debt. -
Dremio Blog: Open Data Insights
Apache Polaris Realms: Multi-Tenancy and Tenant Isolation
A Polaris realm is a top-level administrative boundary that lets one Polaris service host separate sets of principals, roles, catalogs, and configuration. Realms reduce accidental cross-tenant visibility at the catalog layer, but they do not replace network isolation, separate encryption keys, storage policies, database controls, or resource quotas. Production tenancy needs all of those boundaries to agree. -
Dremio Blog: Open Data Insights
Apache Polaris with OIDC: Keycloak, Okta, and External Identity Providers
Apache Polaris can validate OAuth 2.0 access tokens from an external OpenID Connect provider instead of issuing every identity itself. The reliable setup aligns issuer, audience, JWKS, claim mapping, token lifetime, and Polaris role grants. Keycloak and Okta differ in administration, but Polaris sees the same signed token contract. -
Dremio Blog: Open Data Insights
Apache Polaris Generic Tables for Delta Lake and Non-Iceberg Assets
Polaris generic tables register non-Iceberg assets with a name, location, format, and properties so they can participate in catalog discovery and access workflows. They do not turn Delta Lake or another format into Iceberg, coordinate that format's commits, update its transaction log, or automatically vend credentials. The current feature is beta and should be framed as catalog registration, not full table-format governance. -
Dremio Blog: Open Data Insights
Apache Polaris Policies for Compaction, Retention, and Table Maintenance
Apache Polaris policies attach maintenance intent to catalog resources. Built-in policy types cover data compaction, metadata compaction, orphan-file removal, and snapshot expiry. A policy records configuration and inheritance, while a separate execution service must discover, schedule, run, and report the maintenance work. Treat policy storage and task execution as two distinct production components. -
Dremio Blog: Open Data Insights
Apache Polaris with Trino: Configuration, Security, and Operations
Trino connects to Polaris through its Iceberg connector with the REST catalog type. The core settings are the Polaris catalog URI, warehouse name, OAuth credential, token scope, and vended-credential support. Once Polaris grants and storage permissions align, Trino can discover, read, and write the same Iceberg tables used by other REST-compatible engines.
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