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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. At the top of the hierarchy is the metadata file, which stores information about the table’s schema, partition information, and snapshots. tableProperty("format-version", "2").partitionedBy($"product_category").createOrReplace()

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Frequent materialized view refreshes on top of constantly changing base tables due to streamed data can lead to snapshot isolation errors. Also, a data model that allows table truncations at a regular frequency (for example, every 15 seconds) to store only relevant data in tables can cause locking and performance issues.