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Take manual snapshots and restore in a different domain spanning across various Regions and accounts in Amazon OpenSearch Service

AWS Big Data

Snapshots are crucial for data backup and disaster recovery in Amazon OpenSearch Service. These snapshots allow you to generate backups of your domain indexes and cluster state at specific moments and save them in a reliable storage location such as Amazon Simple Storage Service (Amazon S3). Snapshots are not instantaneous.

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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

We will also cover the pattern with automatic compaction through AWS Glue Data Catalog table optimization. Consider a streaming pipeline ingesting real-time event data while a scheduled compaction job runs to optimize file sizes. Transaction 1 successfully updates the tables latest snapshot in the Iceberg catalog from 0 to 1.

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Use open table format libraries on AWS Glue 5.0 for Apache Spark

AWS Big Data

The adoption of open table formats is a crucial consideration for organizations looking to optimize their data management practices and extract maximum value from their data. For more details, refer to Iceberg Release 1.6.1. Branching Branches are independent lineage of snapshot history that point to the head of each lineage.

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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

Iceberg offers distinct advantages through its metadata layer over Parquet, such as improved data management, performance optimization, and integration with various query engines. You can refer to this metadata layer to create a mental model of how Icebergs time travel capability works.

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The AWS Glue Data Catalog now supports storage optimization of Apache Iceberg tables

AWS Big Data

The AWS Glue Data Catalog now enhances managed table optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Along with the Glue Data Catalog’s automated compaction feature, these storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance.

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Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

AWS Big Data

Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios. Referring to the data dictionary and screenshots, its evident that the complete data lineage information is highly dispersed, spread across 29 lineage diagrams. where(outV().as('a')),

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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

Systems of this nature generate a huge number of small objects and need attention to compact them to a more optimal size for faster reading, such as 128 MB, 256 MB, or 512 MB. For more information on streaming applications on AWS, refer to Real-time Data Streaming and Analytics. We use the Hive catalog for Iceberg tables.