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Accelerate your migration to Amazon OpenSearch Service with Reindexing-from-Snapshot

AWS Big Data

In this post, we will introduce a new mechanism called Reindexing-from-Snapshot (RFS), and explain how it can address your concerns and simplify migrating to OpenSearch. Documents are parsed from the snapshot and then reindexed to the target cluster, so that performance impact to the source clusters is minimized during migration.

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Chart Snapshot: Contour Plots

The Data Visualisation Catalogue

Contour Plots allow for the easy identification of maxima, minima, and optimal combinations of X and Y variables that produce desired Z values. Contour plots — Stata The post Chart Snapshot: Contour Plots appeared first on The Data Visualisation Catalogue Blog.

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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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Cloudera Lakehouse Optimizer Makes it Easier Than Ever to Deliver High-Performance Iceberg Tables

Cloudera

Iceberg has many features that drastically reduce the work required to deliver a high-performance view of the data, but many of these features create overhead and require manual job execution to optimize for performance and costs. Compaction is a process that rewrites small files into larger ones to improve performance.

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Optimization Strategies for Iceberg Tables

Cloudera

This blog discusses a few problems that you might encounter with Iceberg tables and offers strategies on how to optimize them in each of those scenarios. Problem with too many snapshots Everytime a write operation occurs on an Iceberg table, a new snapshot is created. See Write properties.

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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. As of this writing, only the optimize-data optimization is supported. To check how to create an Amazon S3 bucket, follow the instructions given here.

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

AWS Big Data

Despite their advantages, traditional data lake architectures often grapple with challenges such as understanding deviations from the most optimal state of the table over time, identifying issues in data pipelines, and monitoring a large number of tables. It is essential for optimizing read and write performance.

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