AWS Big Data Blog

Amazon Redshift made significant strides in 2024, that enhanced price-performance, enabled data lakehouse architectures by blurring the boundaries between data lakes and data warehouses, simplified ingestion and accelerated near real-time analytics, and incorporated generative AI capabilities to build natural language-based applications and boost user productivity. This blog post provides a comprehensive overview of the major product innovations and enhancements made to Amazon Redshift in 2024.

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.

Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and lakehouses. In this post, we discuss the top six game changers that will redefine AWS streaming data.

With Amazon SageMaker Lakehouse unified data connectivity, you can confidently connect, explore, and unlock the full value of your data across AWS services and achieve your business objectives with agility. This post demonstrates how SageMaker Lakehouse unified data connectivity helps your data integration workload by streamlining the establishment and management of connections for various data sources.

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.

Amazon SageMaker Unified Studio, in preview, is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. This post demonstrates how SageMaker Unified Studio unifies your analytic workloads.

Amazon EMR Serverless emerges as a pivotal solution for running streaming workloads, enabling the use of the latest open source frameworks like Spark without the need for configuration, optimization, security, or cluster management. In this post, we highlight some of the key enhancements introduced for streaming jobs.

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.

This post explores robust strategies for maintaining data quality when ingesting data into Apache Iceberg tables using AWS Glue Data Quality and Iceberg branches. We discuss two common strategies to verify the quality of published data. We dive deep into the Write-Audit-Publish (WAP) pattern, demonstrating how it works with Apache Iceberg.