Category: Industries

In this post, we show how you can use graph algorithms to analyze the results of AWS Entity Resolution and related transactions for the CNP use case. We use several AWS services, including Neptune Analytics, AWS Entity Resolution, Amazon SageMaker notebooks, and Amazon S3.

Zupee is a leading skill-based gaming platform offering casual and board games and is one of the fastest growing real money gaming platforms in India. Users can play multiple skill-based games online and win prizes. In this post, we show you how Zupee integrated Amazon Neptune Database to detect anomalies in real time for wallet transactions by creating a system for tracing the complex relationships between users, devices, and wallet transactions metadata.

Habby is a game studio that creates interactive entertainment to connect players worldwide. We adopted Valkey GLIDE, a client library for Amazon ElastiCache for Valkey and Redis OSS, to address our system challenges. Our system uses the Amazon ElastiCache for Redis OSS publish/subscribe (Pub/Sub) functionality for the chat message sending. However, we faced challenges with connection stability during infrastructure changes, such as instance scaling, Redis OSS version upgrades, and hardware failures. This post describes our messaging system architecture and explains how we improved system reliability by using Valkey GLIDE as the client communicating with Amazon ElastiCache.

In this post, we explore how you can achieve a patient 360-degree view using Amazon Neptune and generative AI, and use it to strengthen your organization’s research and breakthroughs. By consolidating information from multiple sources such as electronic health records (EHRs), lab reports, prescriptions, and medical histories into a single location, healthcare providers can gain a better understanding of a patient’s health.

In this post, we discuss how Phreesia used AWS DMS to replicate their on-premises database to AWS in an effective and cost-optimized manner. Because of the database’s large size and complex data structure, properly tuning the AWS DMS configuration was critical to minimize the migration duration and cost. We outline the fine-tuning techniques that were applied to optimize the AWS DMS task settings, instance size, IOPS provisioning, and table mappings. Applying these performance optimizations allowed Phreesia to develop a migration strategy to move this 30 TB database to Amazon S3 in just 2 days without disruption to production workloads.

As industrial and manufacturing companies embark on their digital transformation journey, they are looking to capture and process large volumes of near real-time data for optimizing production, reducing downtime, and improving overall efficiency. As part of this, they’re looking to store data locally at the plant floor or on-premises data center for real-time low-latency reporting […]

Schneider Electric is a leader in digital transformation in energy management and industrial automation. To effectively manage customer account hierarchies in its CRM at scale, Schneider Electric started leveraging advances in generative artificial intelligence (AI) large language models (LLMs) in April 2023. They created a solution to make timely updates to their customer account hierarchies in their CRM by linking customer account information to the correct parent company based on the latest information retrieved from the Internet and proprietary datasets. In this post, we explore further iterations of this project and how the team applied what they learned to the Salesforce CRM system using Amazon Aurora and Amazon Bedrock.

Ola Money is a financial service provided by Ola Financial Services (OFS), which is part of the Ola group of companies. In this post, we share the modernization journey of Ola Money’s MySQL workloads using Amazon Aurora, a relational database management system built for the cloud with MySQL and PostgreSQL compatibility that gives the performance and availability of commercial-grade databases at one-tenth the cost.

Financial services customers choose DynamoDB for security, resilience, performance, and scale. In this post, we discuss how Amazon DynamoDB helps financial services customers overcome these challenges for common industry use cases. We also share customer examples, such as Fidelity Investments, Experian, Moody’s, and other financial services applications that are built with DynamoDB.

Blockchain technology has the potential to revolutionize how personal digital records are managed, stored, and shared, because it offers unique features such as immutability, transparency, security, and decentralization. The application possibilities of blockchain technology in the context of personal digital records encompass various potential use cases, including but not limited to: Create decentralized digital identity […]