AWS Database Blog
Category: Customer Solutions
Zupee implements Amazon Neptune to detect Wallet transaction anomalies in real time
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.
How Habby enhanced resiliency and system robustness using Valkey GLIDE and Amazon ElastiCache
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.
How Amazon Finance Automation built an operational data store with AWS purpose built databases to power critical finance applications
In this post, we discuss how the Amazon Finance Automation team used AWS purpose built databases, such as Amazon DynamoDB, Amazon OpenSearch Service, and Amazon Neptune together coupled with serverless compute like AWS Lambda to build an Operational Data Store (ODS) to store financial transactional data and support FinOps applications with millisecond latency. This data is the key enabler for FinOps business.
How Heroku migrated hundreds of thousands of self-managed PostgreSQL databases to Amazon Aurora
In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Aurora PostgreSQL-Compatible Edition. Heroku completed this migration with no customer impact, increasing platform reliability while simultaneously reducing operational burden. We dive into Heroku and their previous self-managed architecture, the new architecture, how the migration of hundreds of thousands of databases was performed, and the enhancements to the customer experience since its completion.
How Mindbody improved query latency and optimized costs using Amazon Aurora PostgreSQL Optimized Reads
In this post, we highlight the scaling and performance challenges Mindbody was facing due to an increase in their data growth. We also present the root cause analysis and recommendations for adopting to Aurora Optimized Reads, outlining the steps taken to address these issues. Finally, we discuss the benefits Mindbody realized from implementing these changes, including enhanced query performance, significant cost savings, and improved price predictability.
How GaadiBazaar reduced database costs by 40% with Aurora MySQL Serverless
GaadiBazaar draws on over 25 years of vehicle finance expertise from Cholamandalam to connect vehicle buyers and sellers. Their mission is to enable hassle-free transactions at fair prices through buyer-seller interactions and end-to-end financial assistance. This post shows you how GaadiBazaar, an online platform for buying and selling vehicles, achieved significant database cost savings by migrating to Amazon Aurora MySQL Compatible Edition Serverless.
How Aqua Security exports query data from Amazon Aurora to deliver value to their customers at scale
Aqua Security is the pioneer in securing containerized cloud native applications from development to production. Like many organizations, Aqua faced the challenge of efficiently exporting and analyzing large volumes of data to meet their business requirements. Specifically, Aqua needed to export and query data at scale to share with their customers for continuous monitoring and security analysis. In this post, we explore how Aqua addressed this challenge by using aws_s3.query_export_to_s3 function with their Amazon Aurora PostgreSQL-Compatible Edition and AWS Step Functions to streamline their query output export process, enabling scalable and cost-effective data analysis.
How Iterate.ai uses Amazon MemoryDB to accelerate and cost-optimize their workforce management conversational AI agent
Iterate.ai is an enterprise AI platform company delivering innovative AI solutions to industries such as retail, finance, healthcare, and quick-service restaurants. Among its standout offerings is Frontline, a workforce management platform powered by AI, designed to support and empower Frontline workers. Available on both the Apple App Store and Google Play, Frontline uses advanced AI tools to streamline operational efficiency and enhance communication among dispersed workforces. In this post, we give an overview of durable semantic caching in Amazon MemoryDB, and share how Iterate used this functionality to accelerate and cost-optimize Frontline.
How Skello uses AWS DMS to synchronize data from a monolithic application to microservices
Skello is a human resources (HR) software-as-a-service (SaaS) platform that focuses on employee scheduling and workforce management. It caters to various sectors, including hospitality, retail, healthcare, construction, and industry. In this post, we show how Skello uses AWS Database Migration Service (AWS DMS) to synchronize data from an monolithic architecture to microservices and perform data ingestion from the monolithic architecture and microservices to our data lake.
How Orca Security optimized their Amazon Neptune database performance
Orca Security, an AWS Partner, is an independent cybersecurity software provider whose patented agentless-first cloud security platform is trusted by hundreds of enterprises globally. At Orca Security, we use a variety of metrics to assess the significance of security alerts on cloud assets. Our Amazon Neptune database plays a critical role in calculating the exposure of individual assets within a customer’s cloud environment. By building a graph that maps assets and their connectivity between one another and to the broader internet, the Orca Cloud Security Platform can evaluate both how an asset is exposed as well as how an attacker could potentially move laterally within an account. In this post, we explore some of the key strategies we’ve adopted to maximize the performance of our Amazon Neptune database.