Remove Data Processing Remove Metadata Remove Optimization Remove Snapshot
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Amazon OpenSearch Service Under the Hood : OpenSearch Optimized Instances(OR1)

AWS Big Data

Amazon OpenSearch Service recently introduced the OpenSearch Optimized Instance family (OR1), which delivers up to 30% price-performance improvement over existing memory optimized instances in internal benchmarks, and uses Amazon Simple Storage Service (Amazon S3) to provide 11 9s of durability.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift delivers on that needed performance through a number of mechanisms such as caching, automated data model optimization, and automated query rewrites. String-optimized compression The Data Vault 2.0 You can use this mechanism to optimize merge operations while still making the data accessible from within Amazon Redshift.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

This data is sent to Apache Kafka, which is hosted on Amazon Managed Streaming for Apache Kafka (Amazon MSK). Determining optimal table partitioning Determining optimal partitioning for each table is very important in order to optimize query performance and minimize the impact on teams querying the tables when partitioning changes.

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Discover and Explore Data Faster with the CDP DDE Template

Cloudera

See the snapshot below. With HDFS, Solr servers are essentially stateless, so host failures have minimal consequences. HDFS also provides snapshotting, inter-cluster replication, and disaster recovery. . Coordinates distribution of data and metadata, also known as shards. data best served through Apache Solr).

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Iceberg employs internal metadata management that keeps track of data and empowers a set of rich features at scale. The transformed zone is an enterprise-wide zone to host cleaned and transformed data in order to serve multiple teams and use cases. Additionally, you can query in Athena based on the version ID of a snapshot in Iceberg.

Data Lake 102
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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

Although this post uses an Aurora PostgreSQL database hosted on AWS as the data source, the solution can be extended to ingest data from any of the AWS DMS supported databases hosted on your data centers. EMR Serverless includes the Amazon EMR performance-optimized runtime for Apache Spark and Hive.