Category: Industries

In this blog post, AWS introduces a solution to a common challenge in scientific research – the inefficient management of fragmented scientific data. The post demonstrates how the next generation of Amazon SageMaker, through its Unified Studio and Catalog features, helps scientists streamline their workflow by integrating data management and analytics capabilities.

In this post, we demonstrate how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making.

This post explores a framework developed by IBM to modernize baggage analytics using AWS managed services like Amazon Kinesis Data Streams, DynamoDB Streams, and other AWS services within a serverless architecture. The solution enables near real-time baggage operational insights for airlines, delivering cost savings, enhanced scalability, and improved performance while providing better security and operational efficiency to meet evolving airline needs.

In this blog post we shall cover how understanding real-time payout performance, identifying customer behavior patterns across regions, and optimizing internal operations required more than traditional business intelligence and analytics tools. And how since implementing Amazon Redshift and Zero-ETL, MassPay has seen 90% reduction in data availability latency, payments data available for analytics 1.5x faster, leading to 45% reduction in time-to-insight and 37% fewer support tickets related to transaction visibility and payment inquiries.

In this post, we will walk you through how Prime Video used Amazon OpenSearch Service and its AI and machine learning (AI/ML) capabilities to build a more intuitive and enhanced sports search experience.

In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg, we showed how to use Apache Iceberg in the context of strategy backtesting. In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg. Our experiments are based on real-world historical full order book data, provided by our partner CryptoStruct, and compare the trade-offs between these choices, focusing on performance, cost, and quant developer productivity.

HEMA is a household Dutch retail brand name since 1926, providing daily convenience products using unique design. This post describes how HEMA used Amazon DataZone to build their data mesh and enable streamlined data access across multiple business areas. It explains HEMA’s unique journey of deploying Amazon DataZone, the key challenges they overcame, and the transformative benefits they have realized since deployment in May 2024. From establishing an enterprise-wide data inventory and improving data discoverability, to enabling decentralized data sharing and governance, Amazon DataZone has been a game changer for HEMA.

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.

This second post of a two-part series that details how Volkswagen Autoeuropa, a Volkswagen Group plant, together with AWS, built a data solution with a robust governance framework using Amazon DataZone to become a data-driven factory. Part 1 of this series focused on the customer challenges, overall solution architecture and solution features, and how they helped Volkswagen Autoeuropa overcome their challenges. This post dives into the technical details, highlighting the robust data governance framework that enables ease of access to quality data using Amazon DataZone.

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. The data mesh, built on Amazon DataZone, simplified data access, improved data quality, and established governance at scale to power analytics, reporting, AI, and machine learning (ML) use cases. As a result, the data solution offers benefits such as faster access to data, expeditious decision making, accelerated time to value for use cases, and enhanced data governance.