-
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... -
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... -
Camus to Gobblin Migration
Advantages of Migrating to Gobblin Kafka Ingestion Related Job Config Properties Config properties for pulling Kafka topics Config properties for compaction Deployment and Chec... -
EventMesh Runtime Quick Start
EventMesh Runtime Quick Start 1. Binary Distribution Deployment 1.1 Environment 1.2 Download 1.3 Configuration 1.3.1 EventMesh Runtime Configuration 1.3.2 Event Store Configurat... -
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... -
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... -
Queries
Spark Queries To use Iceberg in Spark, first configure Spark catalogs . Iceberg uses Apache Spark’s DataSourceV2 API for data source and catalog implementations. Querying with S... -
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... -
Deployment
Gobblin Execution Modes Overview One important feature of Gobblin is that it can be run on different platforms. Currently, Gobblin can run in standalone mode (which runs on a sing... -
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...
