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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. You can define your own key and value for your resource tag, so that you can easily manage and filter your resources. Tags allows you to assign metadata to your AWS resources.

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Enhance your security posture by storing Amazon Redshift admin credentials without human intervention using AWS Secrets Manager integration

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. You can natively use existing Secrets Manager secrets to access Amazon Redshift using the Amazon Redshift API and query editor.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

Amazon Redshift is a cloud data warehousing service that provides high-performance analytical processing based on a massively parallel processing (MPP) architecture. Building and maintaining data pipelines is a common challenge for all enterprises. All the connection profiles are configured within the dbt profiles.yml file.

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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 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. By adding a metadata layer to data lakes, you get a better user experience, simplified management, and improved performance and reliability on very large datasets.

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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

Within seconds of transactional data being written into supported AWS databases, zero-ETL seamlessly makes the data available in Amazon Redshift, removing the need to build and maintain complex data pipelines that perform extract, transform, and load (ETL) operations. For this post, set this to 8 RPUs. Choose Next.

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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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