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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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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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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

For more examples and references to other posts, refer to the following GitHub repository. In case you don’t have sample data available for testing, we provide scripts for generating sample datasets on GitHub. Querying all snapshots, we can see that we created three snapshots with overwrites after the initial one.

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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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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

The applications must be integrated to the surrounding business systems so ideas can be tested and validated in the real world in a controlled manner. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise. An Overarching Concern: Correctness and Testing. Versioning.

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Evaluating sample Amazon Redshift data sharing architecture using Redshift Test Drive and advanced SQL analysis

AWS Big Data

Redshift Test Drive is a tool hosted on the GitHub repository that let customers evaluate which data warehouse configurations options are best suited for their workload. Generating and accessing Test Drive metrics The results of Amazon Redshift Test Drive can be accessed using an external schema for analysis of a replay.

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Data Observability and Monitoring with DataOps

DataKitchen

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Below we will explain how to virtually eliminate data errors using DataOps automation and the simple building blocks of data and analytics testing and monitoring. . Tie tests to alerts.

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