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

AWS Big Data

Disaster recovery is vital for organizations, offering a proactive strategy to mitigate the impact of unforeseen events like system failures, natural disasters, or cyberattacks. This post focuses on introducing an active-passive approach using a snapshot and restore strategy.

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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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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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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks. Snapshot management allows concurrent data operations without interference, maintaining data consistency across transactions.

Metadata 118
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Amazon OpenSearch Service H1 2023 in review

AWS Big Data

With the search relevance plugin, you can now view a side-by-side comparison of results in OpenSearch Dashboards. SS4O is inspired by both OpenTelemetry and the Elastic Common Schema (ECS) and uses Amazon Elastic Container Service ( Amazon ECS ) event logs and OpenTelemetry (OTel) metadata. Additional field types OpenSearch 2.7

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices. The collected data is available in milliseconds to allow real-time analytics use cases, such as real-time dashboards, real-time anomaly detection, and dynamic pricing.

Analytics 123
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How Gupshup built their multi-tenant messaging analytics platform on Amazon Redshift

AWS Big Data

In this use case, Gupshup is heavily relying on Amazon Redshift as their data warehouse to process billions of streaming events every month, performing intricate data-pipeline-like operations on such data and incrementally maintaining a hierarchy of aggregations on top of raw data.

Analytics 113