AWS Database Blog

Category: Customer Solutions

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.

Vacasa’s migration to Amazon 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 Amazon Relational Database Service (Amazon RDS) for MariaDB to Amazon RDS for MySQL, and finally to Amazon Aurora, highlighting the technical steps taken and the outcomes achieved.

How Monzo Bank reduced cost of TTL from time series index tables in Amazon Keyspaces

At Monzo, we use Amazon Keyspaces (for Apache Cassandra) as our main operational database. Today, we store over 350 TB of data across more than 2,000 tables in Amazon 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 Amazon Keyspaces to reduce our operating costs for an index while preserving its semantics.

FundApps’s journey from SQL Server to Amazon Aurora Serverless v2 with Babelfish

FundApps, founded in 2010, is one of the pioneers in the Regulatory Technology (RegTech) space, which includes compliance monitoring and reporting. FundApps decided to rearchitect their environment and transform it to a cloud-based architecture on AWS to better support the growth of their business. For more information, see Faster, cheaper, greener: Pick three — FundApps modernization journey. In this post, we focus on the persistence layer of the FundApps regulatory data service. You learn how FundApps improved the service scalability, reduced cost, and streamlined operations by migrating from SQL Server database to a cloud-centered solution combining Amazon Aurora Serverless v2 with Babelfish for Aurora PostgreSQL and Amazon Simple Storage Service (Amazon S3).

How the Amazon TimeHub team designed a recovery and validation framework for their data replication framework: Part 4

With AWS DMS, you can use data validation to make sure your data was migrated accurately from the source to the target. If you enable validation for a task, AWS DMS begins comparing the source and target data immediately after a full load is performed for a table. In this post, we describe the custom framework we built on top of AWS DMS validation tasks to maintain data integrity as part of the ongoing replication between source and target databases.

How the Amazon TimeHub team handled disruption in AWS DMS CDC task caused by Oracle RESETLOGS: Part 3

In How the Amazon TimeHub team designed resiliency and high availability for their data replication framework: Part 2, we covered different scenarios handling replication failures at the source database (Oracle), AWS DMS, and target database (Amazon Aurora PostgreSQL-Compatible Edition). As part of our resilience scenario testing, when there was a failover between the Oracle primary database instance and primary standby instances, and the database opened up with RESETLOGS, AWS DMS couldn’t automatically read the new set of logs in case of a new incarnation. In this post, we dive deep into the solution the Amazon TimeHub team used for detecting such a scenario and recovering from it. We then describe the post-recovery steps to validate and correct data discrepancies caused due to the failover scenario.