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Apache Iceberg is an open-source table format for large analytic datasets, letting multiple engines query and manage huge tables on object storage.
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Databricks says it added UNREGISTER to the Apache Iceberg REST catalog spec, so a table can change catalogs with zero files copied. A team can only use it if the catalog it is leaving implements the call, and the post demonstrates it on Unity Catalog.
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Cloudflare made its Data Platform generally available as Basin, three serverless services that ingest, catalog and query Apache Iceberg tables on R2. Outside Iceberg engines read the same tables, so Workers teams can price Basin SQL against a warehouse on identical data.
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AWS has embedded DuckDB in Aurora PostgreSQL from versions 17.11 and 18.6, so one SQL query can join live rows with Iceberg and Parquet tables in S3. Reverse-ETL jobs that exist only for that join can be retired if the database instance has room for the lake scans.
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Apache Iceberg's REST Catalog spec now lets a catalog return per-user row filters and nine column actions for a trusted engine to enforce, Databricks says. Policies that need subqueries or lookup tables do not fit that vocabulary, so the portability covers simple rules running on engines a deployment already trusts.
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Amazon S3 Tables now supports every Apache Iceberg V3 data type after adding four, including geometry, geography and nanosecond timestamps. Telemetry and mapping teams can stop storing event times and coordinates as integers and strings.
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AWS Community Builder Aki argues Apache Iceberg made data portable while permissions, auditing, lineage and sharing stay tied to each catalog. The spec leaves the atomic commit to each catalog, so the catalog is now the choice that is hard to reverse.
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Rust's Iceberg REST client ran up to 4.29x faster than pyiceberg on a local catalog, with 85% to 90% of the gap in Python's HTTP library. pyiceberg covers 21 of 25 tested endpoints to Rust's 13, so coverage settles the choice before speed does.
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Apache Iceberg's Rust REST client, iceberg-catalog-rest 0.10.1, implements 13 of 25 tested catalog operations and logs in to five of seven catalogs. The two it cannot log in to are AWS Glue and S3 Tables, and reaching them from Rust means a different crate with a different operation set.
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Reviewers logged 10,991 passing tests on Iceberg's first 1.12.0 candidate before rejecting it over two vendored web files. The change most likely to break a downstream build is a removed partition-statistics helper.
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Keeping the Iceberg table format governance-unaware was a deliberate call. That leaves the catalog as the only thing standing on the path of every read. A dev.to guide lists six pieces an operator has to source above it.
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A runnable two-agent example moves every refusal into the catalog that vends credentials, because a key covers a storage prefix and there are no rows at the point of enforcement. The author works on that catalog.
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A dev.to post argues that Snowflake-managed Iceberg tables let you keep the managed engine while owning the Parquet in your own bucket. The same post says the catalog stays Snowflake's, and that 50,000-inserts-per-second ingest should not move.
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AWS has published a pipeline that pulls Bedrock guardrail traces out of model invocation logs, rewrites them as OCSF Detection Findings, and lands them in the CloudWatch store where analysts already query CloudTrail and VPC Flow Logs.
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A read-only MCP server with four Iceberg tools ran against six live catalogs off a single environment variable. Three of the tools worked on pyiceberg alone, and the fourth needed a storage package for the object store.
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RustFS gives you an S3 endpoint you start with one docker run, while SeaweedFS gives you a file system that also speaks S3. A dev.to comparison puts the dividing line at POSIX mounts and the count of tiny files.
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AWS schedules compaction, snapshot expiration and orphan cleanup on table buckets itself, and a dev.to teardown finds that managed maintenance halts on any table carrying a user-defined tag or branch.
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A step-by-step build of the same Apache Iceberg data agent on Google ADK, AWS Strands and Microsoft Agent Framework measures what a cloud move costs in code, in latency and in storage wiring.
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A GCP walkthrough put Spark, Trino and BigQuery on the same orders table through Google's Iceberg REST catalog. The 409 ConflictException it logged is the part of the demo a production pipeline has to plan for.
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KeewanoDB keeps events in the order they arrived, grouped around a customer or device, so an agent can read one entity's history without an ETL job. Keewano expects the warehouse to stay where it is.
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At Dreamforce 2026 the company said Model Context Protocol will carry its context and actions into both hyperscalers' agent products, while AWS agents move into Slack. The connector work becomes a scoping job.
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