-
Java Custom Catalog
Custom Catalog It’s possible to read an iceberg table either from an hdfs path or from a hive table. It’s also possible to use a custom metastore in place of hive. The steps to do... -
Evolution
Evolution Iceberg supports in-place table evolution . You can evolve a table schema just like SQL — even in nested structures — or change partition layout when data volume chang... -
Glue Data Catalog
Syncing to Glue Data Catalog This document walks through the steps to register an Apache XTable™ (Incubating) synced table in Glue Data Catalog on AWS. Pre-requisites Source ta... -
Flink Connector
Flink Connector Apache Flink supports creating Iceberg table directly without creating the explicit Flink catalog in Flink SQL. That means we can just create an iceberg table by s... -
Daft
Daft Daft is a distributed query engine written in Python and Rust, two fast-growing ecosystems in the data engineering and machine learning industry. It exposes its flavor of t... -
Mixed-Hive
Mixed-Hive format is a format that has better compatibility with Hive than Mixed-Iceberg format. Mixed-Hive format uses a Hive table as the BaseStore and an Iceberg table as the C... -
Features and Limitations
Features and Limitations Features Apache XTable™ (Incubating) provides users with the ability to translate metadata from one table format to another. Apache XTable™ (Incubatin... -
Flink DDL
DDL commands CREATE Catalog Hive catalog This creates an Iceberg catalog named hive_catalog that can be configured using 'catalog-type'='hive' , which loads tables from Hive m... -
RisingWave
RisingWave RisingWave is a Postgres-compatible SQL database designed for real-time event streaming data processing, analysis, and management. It can ingest millions of events per... -
Apache Spark
Querying from Apache Spark To read an Apache XTable™ (Incubating) synced target table (regardless of the table format) in Apache Spark locally or on services like Amazon EMR, Goog...
