AWS Database Blog
Category: Customer Solutions
How HAQM Finance Automation built an operational data store with AWS purpose built databases to power critical finance applications
In this post, we discuss how the HAQM Finance Automation team used AWS purpose built databases, such as HAQM DynamoDB, HAQM OpenSearch Service, and HAQM 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 HAQM Aurora
In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on HAQM Elastic Compute Cloud (HAQM EC2) to HAQM 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 HAQM 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 HAQM Aurora MySQL Compatible Edition Serverless.
How Aqua Security exports query data from HAQM 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 HAQM 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 HAQM 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 HAQM 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 HAQM 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 HAQM 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 HAQM Neptune database.
Vacasa’s migration to HAQM Aurora for a more efficient Property Management System
Vacasa is North America’s leading vacation rental management platform, revolutionizing the rental experience with advanced technology and expert teams. In the competitive short-term vacation property management industry, efficient systems are critical. To maintain its edge and continue providing top-notch service, Vacasa needed to modernize its primary transactional database to improve performance, provide high availability, and reduce costs. In this post, we share Vacasa’s journey from HAQM Relational Database Service (HAQM RDS) for MariaDB to HAQM RDS for MySQL, and finally to HAQM Aurora, highlighting the technical steps taken and the outcomes achieved.
How Monzo Bank reduced cost of TTL from time series index tables in HAQM Keyspaces
At Monzo, we use HAQM Keyspaces (for Apache Cassandra) as our main operational database. Today, we store over 350 TB of data across more than 2,000 tables in HAQM Keyspaces, handling over 2,000,000 reads and 100,000 writes per second at peak. In this post, we share how we used a different mechanism for row expiry than the Time to Live setting in HAQM Keyspaces to reduce our operating costs for an index while preserving its semantics.