AWS Database Blog

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.

Monitor the health of Amazon Aurora PostgreSQL instances in large-scale deployments

In this post, we show you how to achieve better visibility into the health of your Amazon Aurora PostgreSQL instances, proactively address potential issues, and maintain the smooth operation of your database infrastructure. The solution is designed to scale with your deployment, providing robust and reliable monitoring for even the largest fleets of instances.

Oracle Application Express for Amazon RDS for Oracle demystified

Oracle Application Express (APEX) allows you to quickly develop and deploy compelling applications that solve real problems and provide immediate value. In this post, we cover the steps for installing, configuring, and upgrading an APEX repository in Amazon RDS for Oracle and ORDS. We also show how to handle APEX when performing snapshot restore or point-in-time recovery (PITR).

Introducing the GraphRAG Toolkit

Amazon Neptune recently released the GraphRAG Toolkit, an open source Python library that makes it straightforward to build graph-enhanced Retrieval Augmented Generation (RAG) workflows. In this post, we describe how you can get started with the toolkit. We begin by looking at the benefits of adding a graph to your RAG application. Then we show you how to set up a quick start environment and install the toolkit. Lastly, we discuss some of the design considerations that led to the toolkit’s graph model and its approach to content retrieval.

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.

Diving deep into the new Amazon Aurora Global Database writer endpoint

On October 22, 2024, we announced the availability of the Aurora Global Database writer endpoint, a highly available and fully managed endpoint for your global database that Aurora automatically updates to point to the current writer instance in your global cluster after a cross-Region switchover or failover, alleviating the need for application changes and simplifying routing requests to the writer instance. In this post, we dive deep into the new Global Database writer endpoint, covering its benefits and key considerations for using it with your applications.

Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 2: Enhancing fraud detection with Parquet and CSV import and export

In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. In Part 1, we introduced the import and export functionalities, and walked you through how to quickly get started with them. In this post, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.

Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 1: Introducing Parquet and CSV import and export

In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. Part 1 introduces the import and export functionalities, and walks you through how to quickly get started with them. In Part 2, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.

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.