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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.

Data Lake 114
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How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

Time Travel: Reproduce a query as of a given time or snapshot ID, which can be used for historical audits and rollback of erroneous operations, as an example. 4 2005 7140596. We see that as of the first snapshot ( 7445571238522489274) we had data from the years 1995 to 2005 in the table. 1 2008 7009728. 2 2007 7453215.

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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. For the template and setup information, refer to Test Your Streaming Data Solution with the New Amazon Kinesis Data Generator. We use two datasets in this post.