Remove Data Warehouse Remove Management Remove Metadata Remove Snapshot
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Manage your data warehouse cost allocations with Amazon Redshift Serverless tagging

AWS Big Data

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. Tags allows you to assign metadata to your AWS resources. You can define your own key and value for your resource tag, so that you can easily manage and filter your resources.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more. AWS provides integrations for various AWS services with Iceberg tables as well, including AWS Glue Data Catalog for tracking table metadata.

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

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

Introduction For more than a decade now, the Hive table format has been a ubiquitous presence in the big data ecosystem, managing petabytes of data with remarkable efficiency and scale. They also provide a “ snapshot” procedure that creates an Iceberg table with a different name with the same underlying data.

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Benefits of Enterprise Modeling and Data Intelligence Solutions

erwin

a senior business process management architect at a pharma/biotech company with more than 5,000 employees, erwin Evolve was useful for enterprise architecture reference. As he put it, “We are describing our business process and we are trying to describe our data catalog. Data Modeling with erwin Data Modeler. George H.,