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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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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

Metadata layer Contains metadata files that track table history, schema evolution, and snapshot information. In many operations (like OVERWRITE, MERGE, and DELETE), the query engine needs to know which files or rows are relevant, so it reads the current table snapshot. This is optional for operations like INSERT.

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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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Proposals for model vulnerability and security

O'Reilly on Data

Data poisoning refers to someone systematically changing your training data to manipulate your model’s predictions. Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. Data poisoning attacks. Watermark attacks.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

For more information, refer SQL models. Snapshots – These implements type-2 slowly changing dimensions (SCDs) over mutable source tables. Tests – These are assertions you make about your models and other resources in your dbt project (such as sources, seeds, and snapshots). For more information, refer to Redshift set up.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. For more information, refer to Amazon S3: Allows read and write access to objects in an S3 Bucket.

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