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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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Achieve data resilience using Amazon OpenSearch Service disaster recovery with snapshot and restore

AWS Big Data

This post focuses on introducing an active-passive approach using a snapshot and restore strategy. Snapshot and restore in OpenSearch Service The snapshot and restore strategy in OpenSearch Service involves creating point-in-time backups, known as snapshots , of your OpenSearch domain.

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

AWS Big 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. An Iceberg table’s metadata stores a history of snapshots, which are updated with each transaction. We highlight its notable updates in this section.

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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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Unleash the power of Snapshot Management to take automated snapshots using Amazon OpenSearch Service

AWS Big Data

in Amazon OpenSearch Service , we introduced Snapshot Management , which automates the process of taking snapshots of your domain. Snapshot Management helps you create point-in-time backups of your domain using OpenSearch Dashboards, including both data and configuration settings (for visualizations and dashboards).

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Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality

AWS Big Data

Heres how it works: As data streams in, it passes through a validation process Valid data is written directly to the table referred by downstream users Invalid or problematic data is redirected to a separate DLQ for later analysis and potential recovery The following screenshot shows this flow.

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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

In this post, we use the term vanilla Parquet to refer to Parquet files stored directly in Amazon S3 and accessed through standard query engines like Apache Spark, without the additional features provided by table formats such as Iceberg. When a user requests a time travel query, the typical workflow involves querying a specific snapshot.

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