AWS Database Blog
Category: HAQM Neptune
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.
Using generative AI and HAQM Bedrock to generate SPARQL queries to discover protein functional information with UniProtKB and HAQM Neptune
In this post, we demonstrate how to use generative AI and HAQM Bedrock to transform natural language questions into graph queries to run against a knowledge graph. We explore the generation of queries written in the SPARQL query language, a well-known language for querying a graph whose data is represented as Resource Description Framework (RDF).
Create a 360-degree master data management patient view solution using HAQM Neptune and generative AI
In this post, we explore how you can achieve a patient 360-degree view using HAQM 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.
Introducing the GraphRAG Toolkit
HAQM 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.
Use HAQM 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 HAQM 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 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.
How Coinbase provides trustworthy financial experiences through real-time user clustering with HAQM Neptune
In this post, we discuss how Coinbase migrated their user clustering system to HAQM Neptune Database, enabling them to solve complex and interconnected data challenges at scale.
How Apollo Tyres built their tyre genealogy solution using HAQM Neptune and HAQM Bedrock
This is a joint post co-authored with Shailender Gupta, Global Head of Data Engineering, Reporting and Analytics at Apollo Tyres Apollo Tyres, headquartered in Gurgaon, India, is a prominent global tyre manufacturer with production facilities in India and Europe. The company has a widespread presence, selling tyres to consumers and industrial customers across over 100 […]
How HAQM stores deliver trustworthy shopping and seller experiences using HAQM Neptune
Nearly three decades ago, HAQM set out to be Earth’s most customer-centric company, where people can discover and purchase the widest possible selection of safe and authentic goods. When a customer makes a purchase in our store, they trust they will receive an authentic product, whether the item is sold by HAQM Retail or by one […]