Amazon Redshift now supports row-level UPDATE, DELETE, and MERGE operations on Apache Iceberg tables. Customers who use Iceberg to build interoperable data lakes can now perform data manipulation language (DML) operations directly from Amazon Redshift, without moving data to external processing engines. Previously, modifying individual rows in Iceberg tables required using separate engines, adding complexity and latency to data pipelines.
With this launch, you can run UPDATE, DELETE, and MERGE (UPSERT) statements on both partitioned and unpartitioned Iceberg tables, including S3 Tables. Supported Iceberg partition transforms include identity, bucket, truncate, year, month, day, and hour. MERGE enables you to combine insert and update logic in a single statement for common data integration patterns such as change data capture and slowly changing dimensions. Tables modified by Redshift are compatible with other Iceberg-compatible engines, including Amazon EMR and Amazon Athena, preserving cross-engine interoperability. AWS Lake Formation permissions are supported for Iceberg write operations.
Amazon Redshift support for UPDATE, DELETE, and MERGE commands on Apache Iceberg tables is available in all AWS Regions where Amazon Redshift is available. To get started, visit the Writing to Apache Iceberg tables section in the Amazon Redshift Database Developer Guide, where you will also find documentation for the SQL syntax.
Categories: marketing:marchitecture/analytics,general:products/amazon-redshift
Source: Amazon Web Services
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