Category: Database

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).

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.

Learn how to migrate Google BigQuery workloads to an open lakehouse on AWS built on Apache Iceberg and Amazon S3. This post explains why an open lakehouse future-proofs your data, presents the target architecture on AWS, and walks through a step-by-step migration: assess, design, convert, migrate, cut over, and optimize.

Learn how to set up Amazon Redshift Data Sharing between a producer and consumer cluster and use dbt Cloud to transform the shared data into analytics-ready models, with live, zero-copy access and no ETL between clusters.

Amazon Redshift now automatically resolves many concurrent writes to Apache Iceberg tables that don’t actually overlap, committing changes that previously failed and had to retry. Learn why these writes conflict, how the engine rebases non-overlapping ones across operation, partition, and file checks, and the throughput gains it delivers.

Amazon Redshift now supports Iceberg materialized views. Compute an aggregation once in Amazon Redshift and store the result as a standard Apache Iceberg table in Amazon S3, queryable by Amazon Athena, Apache Spark, Amazon SageMaker, and AWS Glue. This post covers use cases, incremental refresh, and a step-by-step getting-started guide.

Amazon Redshift automates much of its own tuning, but periodic operational reviews still pay off. Learn how the review_cluster tool in the open-source Amazon Redshift MCP server runs a full cluster diagnostic from one natural-language request, evaluating 12 diagnostic areas and returning prioritized, documentation-linked recommendations in minutes.

Amazon Aurora PostgreSQL zero-ETL integration with Amazon SageMaker replicates your operational data to a lakehouse in near real time, without building custom ETL pipelines. Learn the architecture and change data capture mechanics, then set up the integration and query your data in Amazon SageMaker.

Amazon Redshift now supports evolving Apache Iceberg table schemas and partition layouts through ALTER statements, with no data rewrites or pipeline rebuilds. In this final post of the series, you rename, add, drop, and widen columns, evolve partitions, and create AWS Lake Formation resource links for governed cross-engine access to Amazon S3 Tables.

Learn how United Airlines uses AWS Glue Data Catalog federation to query Databricks Unity Catalog data directly from Amazon Redshift Serverless without duplicating data, using resource links and AWS Lake Formation for governance.