Remove Reference Remove Snapshot Remove Testing
article thumbnail

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.

Snapshot 110
article thumbnail

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.

Metadata 111
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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.

Metadata 105
article thumbnail

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).

Snapshot 109
article thumbnail

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.

IT 363
article thumbnail

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.

Testing 111
article thumbnail

In-place version upgrades for applications on Amazon Managed Service for Apache Flink now supported

AWS Big Data

Refer to Upgrading Applications and Flink Versions for more information about how to avoid any unexpected inconsistencies. The next recommended step is to test your application locally with the newly upgraded Apache Flink runtime. If you’re using Gradle, refer to How to use Gradle to configure your project.

Snapshot 113