Category: Amazon Redshift

DeNA Co., Ltd. (DeNA) engages in a variety of businesses, from games and live communities to sports & the community and healthcare & medical, under our mission to delight people beyond their wildest dreams. This post introduces a case study where DeNA combined Amazon Redshift Serverless and dbt (dbt Core) to accelerate data quality tests in their business.

In this post, we use dbt for data modeling on both Amazon Athena and Amazon Redshift. dbt on Athena supports real-time queries, while dbt on Amazon Redshift handles complex queries, unifying the development language and significantly reducing the technical learning curve. Using a single dbt modeling language not only simplifies the development process but also automatically generates consistent data lineage information. This approach offers robust adaptability, easily accommodating changes in data structures.

In this post, we continue from Accelerate Amazon Redshift secure data use with Satori – Part 1, and explain how Satori, an Amazon Redshift Ready partner, simplifies both the user experience of gaining access to data and the admin practice of granting and revoking access to data in Amazon Redshift. Satori enables both just-in-time and self-service access to data.

In this post, we explore the process of federating into AWS using Microsoft Entra ID and AWS Identity and Access Management (IAM), and how to restrict access to datasets based on permissions linked to AD groups. We guide you through the setup process, and demonstrate how to seamlessly connect to the Redshift Query Editor while making sure data access permissions are accurately enforced based on your Microsoft Entra ID groups.

Amazon SageMaker Lakehouse offers a unified solution for enterprise data access, combining data from warehouses and lakes. This post demonstrates how SageMaker Lakehouse integrates scattered data sources, enabling secure enterprise-wide access, and allowing teams to use their preferred tools for predicting and analyzing customer churn. The solution involves multiple data sources, including Amazon S3, Amazon Redshift, and AWS Glue Data Catalog, with AWS Lake Formation managing permissions.

ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division.

The AWS Glue Data Catalog now automates generating statistics for new tables. These statistics are integrated with the cost-based optimizer (CBO) from Amazon Redshift Spectrum and Amazon Athena, resulting in improved query performance and potential cost savings. In this post, we discuss how the Data Catalog automates table statistics collection and how you can use it to enhance your data platform’s efficiency.

Now, we are announcing general availability (GA) of Amazon Redshift multi-data warehouse writes through data sharing. This new capability allows you to scale your write workloads and achieve better performance for extract, transform, and load (ETL) workloads by using different warehouses of different types and sizes based on your workload needs.

In this post, we explore how to use Aurora MySQL-Compatible Edition Zero-ETL integration with Amazon Redshift and dbt Cloud to enable near real-time analytics. By using dbt Cloud for data transformation, data teams can focus on writing business rules to drive insights from their transaction data to respond effectively to critical, time sensitive events.

In this post, we’ll walk through an example ETL process that uses session reuse to efficiently create, populate, and query temporary staging tables across the full data transformation workflow—all within the same persistent Amazon Redshift database session. You’ll learn best practices for optimizing ETL orchestration code, reducing job runtimes by eliminating connection overhead, and simplifying pipeline complexity