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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.

Snapshot 104
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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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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. We take care of the ETL for you by automating the creation and management of data replication. Glue ETL offers customer-managed data ingestion.

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Data Observability and Monitoring with DataOps

DataKitchen

That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. We see data observability as a component of DataOps. In our definition of data observability, we put the focus on the important goal of eliminating data errors.

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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

In this blog post, we dive into different data aspects and how Cloudinary breaks the two concerns of vendor locking and cost efficient data analytics by using Apache Iceberg, Amazon Simple Storage Service (Amazon S3 ), Amazon Athena , Amazon EMR , and AWS Glue.

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Patterns for updating Amazon OpenSearch Service index settings and mappings

AWS Big Data

Use the reindex API operation The _reindex operation snapshots the index at the beginning of its run and performs processing on a snapshot to minimize impact on the source index. The source index can still be used for querying and processing the data. Mikhail specializes in data analytics services.

Snapshot 106
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Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

AWS Big Data

resource(“dynamodb”) table = dynamodb.Table(dydb_lookup_table) response = table.scan() items = response[“Items”] jsondata = sc.parallelize(items) lookupDf = glueContext.read.json(jsondata) return lookupDf # Load the Amazon Kinesis data stream from Amazon Glue Data Catalog. def readDynamoDb(): dynamodb = boto3.resource(“dynamodb”)