Remove Data Processing Remove Interactive Remove Metadata Remove Snapshot
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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

In this post, we show you how you can convert existing data in an Amazon S3 data lake in Apache Parquet format to Apache Iceberg format to support transactions on the data using Jupyter Notebook based interactive sessions over AWS Glue 4.0. AWS Command Line Interface (AWS CLI) configured to interact with AWS Services.

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

AWS Big Data

Performance It is not uncommon for sub-second SLAs to be associated with data vault queries, particularly when interacting with the business vault and the data marts sitting atop the business vault. Chargeback metadata Amazon Redshift provides different pricing models to cater to different customer needs.

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Introducing AWS Glue crawler and create table support for Apache Iceberg format

AWS Big Data

Iceberg captures metadata information on the state of datasets as they evolve and change over time. AWS Glue crawlers will extract schema information and update the location of Iceberg metadata and schema updates in the Data Catalog. Choose Create.

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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. . The dashboard applications in HUE use standard Solr APIs and can interact with data indexed and stored in HDFS.

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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.

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Amazon OpenSearch Service Under the Hood : OpenSearch Optimized Instances(OR1)

AWS Big Data

Today, customers widely use OpenSearch Service for operational analytics because of its ability to ingest high volumes of data while also providing rich and interactive analytics. In such an event, the new instance family guarantees recovery of both the cluster metadata and the index data up to the latest acknowledged operation.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

We introduce you to Amazon Managed Service for Apache Flink Studio and get started querying streaming data interactively using Amazon Kinesis Data Streams. Frequent materialized view refreshes on top of constantly changing base tables due to streamed data can lead to snapshot isolation errors.