https://aws.amazon.com/blogs/big-data/store-and-process-large-strings-in-amazon-redshift-super-columns/ https://docs.aws.amazon.com/redshift/latest/dg/r_enable_large_strings_opt_in.html

Amazon Redshift now supports multi-statement transactions for Apache Iceberg tables, allowing multiple DML statements to be published as a single atomic snapshot instead of committing each statement separately.

Groups Conversations All groups and messages Sign in     Assess and track your Amazon Redshift RA3 to RG modernization with CUDOS 0 views Eyal Estrin unread, 12:19 PM (27 minutes ago)    to https://aws.amazon.com/blogs/big-data/assess-and-track-your-amazon-redshift-ra3-to-rg-modernization-with-cudos/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Groups Conversations All groups and messages Sign in Technical - Cloud Conversations About Privacy • Terms     Define metrics once and keep them correct with the dbt Semantic Layer on Amazon Redshift 0 views Eyal Estrin unread, 10:19 AM (27 minutes ago)    to https://aws.amazon.com/blogs/big-data/define-metrics-once-and-keep-them-correct-with-the-dbt-semantic-layer-on-amazon-redshift/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Amazon Redshift now supports multi-statement transactions for Apache Iceberg tables, so you can run several DML statements against one Iceberg table inside a single BEGIN…COMMIT and publish them as one atomic Iceberg snapshot. This post explains why one commit per statement makes multi-step pipelines fragile, what you can run in a transaction, and how it performs.

Amazon Redshift has no native semantic layer, yet teams still need consistent, governed metric definitions across BI tools. This post shows how to define a metric once with the open-source dbt Semantic Layer (MetricFlow) on Amazon Redshift so it stays correct at any grain, and proves a non-additive weekly average comes out right ($280, not $550).

CUDOS dashboard now includes Amazon Redshift RA3-to-RG migration insights to help assess cost savings, track adoption progress, and quantify efficiency gains from upgrading to Graviton-based RG instances.

The CUDOS 5.10 dashboard adds an Amazon Redshift RA3-to-RG section that helps you assess the cost-efficiency opportunity of moving to Graviton-based RG instances, track adoption as your instance-generation mix shifts, and quantify the gains already achieved on RG.

This article demonstrates how to use the dbt Semantic Layer with MetricFlow on Amazon Redshift to define non-additive metrics like average order value once and compute them correctly across arbitrary dimension combinations at query time.

After an Amazon Aurora Global Database switchover or failover, the Aurora zero-ETL integration with Amazon Redshift keeps referencing the former primary and stops ingesting. Learn how to automate integration recreation with Amazon EventBridge and AWS Lambda so your analytics pipeline recovers without manual intervention.

Learn how AWS Emulator is transforming serverless application testing by enabling fast, secure, and AI-friendly AWS integration testing without requiring a cloud account.

Today, AWS announces a new bi-directional, zero-copy integration between AWS Analytics services and Workday Data Cloud. Instead of building and maintaining ETL pipelines to replicate data between systems, joint customers can now query across their AWS and Workday datasets in place, without moving or copying data, while retaining existing security controls in both environments. From AWS, you can use Amazon SageMaker Unified Studio, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon Athena to query Workday data through Workday Live Data Query or federate to Apache Iceberg tables in Workday Data Cloud through catalog federation. Configure a connection in SageMaker Unified Studio, AWS Glue, or AWS Lake Formation consoles and begin querying immediately. Access is governed by AWS and Workday permissions. From Workday, you can register your AWS Glue Data Catalog Iceberg REST endpoint as an external catalog in Workday Analytics and query your AWS Iceberg tables directly. Common use cases include joining workforce data with business data for talent analytics, workforce planning, and machine learning. This integration is available in all AWS commercial regions where AWS Glue is available. To learn more, see the AWS Glue User Guide, the Workday Data Cloud integration with AWS on Workday Data Cloud, and the AWS Big Data Blog post.

Today, AWS announces a new bi-directional, zero-copy integration between AWS Analytics services and Workday Data Cloud. Instead of building and maintaining ETL pipelines to replicate data between systems, joint customers can now query across their AWS and Workday datasets in place, without moving or copying data, while retaining existing security controls in both environments. From AWS, you can use Amazon SageMaker Unified Studio, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon Athena to query Workday data through Workday Live Data Query or federate to Apache Iceberg tables in Workday Data Cloud through catalog federation. Configure a connection in SageMaker Unified Studio, AWS Glue, or AWS Lake Formation consoles and begin querying immediately. Access is governed by AWS and Workday permissions. From Workday, you can register your AWS Glue Data Catalog Iceberg REST endpoint as an external catalog in Workday Analytics and query your AWS Iceberg tables directly. Common use cases include joining workforce data with business data for talent analytics, workforce planning, and machine learning. This integration is available in all AWS commercial regions where AWS Glue is available. To learn more, see the AWS Glue User Guide, the Workday Data Cloud integration with AWS on Workday Data Cloud, and the AWS Big Data Blog post.

Every data platform now ships agents, skills and MCP servers, but nobody can tell you which AI agent to trust with your warehouse. Day 1 of an open-source project to change that: a neutral decider that runs real tasks, lets the platform grade the agent, and tracks correctness, failed queries, cost and tokens. It starts with Amazon Redshift and aims at warehouses and lakehouses. Run it on your own account.

Amazon Redshift now supports individual strings up to 16 million bytes within SUPER data types, expanding from the previous 65,535 byte limit.

Explore how to deploy OpenMetadata on AWS and build a centralized data catalog for your environment. This hands-on guide covers cloud-native architecture, metadata ingestion, data discovery, lineage, governance, and data quality, integrating services such as Amazon S3, Redshift, RDS, AWS Glue, and OpenSearch.

Amazon Redshift SUPER data type now supports storing and processing large strings up to 16 million bytes, enables in-memory string operations without truncation, and works with case-insensitive databases for easier migrations.

Today, AWS announces a new bi-directional, zero-copy integration between AWS Analytics services and Workday Data Cloud. Instead of building and maintaining ETL pipelines to replicate data between systems, joint customers can now query across their AWS and Workday datasets in place, without moving or copying data, while retaining existing security controls in both environments. From AWS, you can use Amazon SageMaker Unified Studio, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon Athena to query Workday data through Workday Live Data Query or federate to Apache Iceberg tables in Workday Data Cloud through catalog federation. Configure a connection in SageMaker Unified Studio, AWS Glue, or AWS Lake Formation consoles and begin querying immediately. Access is governed by AWS and Workday permissions. From Workday, you can register your AWS Glue Data Catalog Iceberg REST endpoint as an external catalog in Workday Analytics and query your AWS Iceberg tables directly. Common use cases include joining workforce data with business data for talent analytics, workforce planning, and machine learning. This integration is available in all AWS commercial regions where AWS Glue is available. To learn more, see the https://docs.aws.amazon.com/glue/latest/dg/connecting-to-data-workday.html, the https://doc.workday.com/admin-guide/en-us/workday-data-cloud/data-out/workday-data-lake/workday-data-lake.html, and the https://aws.amazon.com/blogs/big-data/introducing-the-workday-data-cloud-integration-with-aws/.

https://aws.amazon.com/redshift/ now supports individual strings up to 16 million bytes within https://docs.aws.amazon.com/redshift/latest/dg/r_SUPER_type.html. Once you opt in through a database parameter, this enhancement applies when ingesting structured or semi-structured data from JSON, PARQUET, TEXT, and CSV source files, and when transforming data through extract load and transform (ELT) processes. Previously, individual strings within SUPER data types were limited to 65,535 bytes. Storing large individual strings in the SUPER data type helps avoid complex pre-loading transformations needed to store the source data in Amazon Redshift. You can now directly ingest large individual strings greater than 65,535 bytes without extra processing (for example, lengthy documents like terms and conditions and contracts), making them queryable using SQL. Once the data is in Amazon Redshift, you can use the flexibility of PartiQL language to explore and navigate through the data or use string functions to transform the data. Functions such as https://docs.aws.amazon.com/redshift/latest/dg/JSON_PARSE.html, https://docs.aws.amazon.com/redshift/latest/dg/r_CONCAT.html, and https://docs.aws.amazon.com/redshift/latest/dg/r_LISTAGG.html can now take input strings and return output strings up to 16 million bytes in length. This enhancement is available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. See the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ for more details. To learn more, please visit our https://docs.aws.amazon.com/redshift/latest/dg/r_enable_large_strings_opt_in.html or http://aws.amazon.com/blogs/big-data/store-and-process-large-strings-in-amazon-redshift-super-columns.