Learn how you can use Amazon Neptune’s Graph Database to simplify building and running graph applications.
When Amazon Web Services announced the general availability of Amazon Neptune, thousands of customers, including Samsung, Intuit and Pearson, previewed the database and used it to build social networks, recommendation engines, fraud detection, knowledge graphs and drug discovery applications, according to a press release.
“Amazon Neptune efficiently stores and navigates highly connected data, allowing developers to create sophisticated, interactive graph applications that can query billions of relationships with millisecond latency,” the release continued.
Here are six ways customers can use the AWS graph database, according to AWS.
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Amazon Neptune is a fast, reliable and fully managed graph database service that aims to make it easier for enterprises to build and run applications on highly connected datasets. It enables developers to create complex interactive graph applications capable of querying billions of relationships with millisecond latency.
In addition, the database helps analyze complex data relationships and supports popular graph models Property Graph and W3C’s RDF and their respective query languages, Apache TinkerPop Gremlin and SPARQL, so developers can quickly build queries that can navigate complex datasets.
Graph databases like Neptune are useful for IT pros who find themselves managing growing volumes of data and want to more easily gain insights from this information. Other companies use these databases for tasks like identity access management and master data management.
Those interested in getting started with Amazon Neptune will find that it does not require upfront costs, licenses or commitments — customers pay only for the resources they use.
Amazon Neptune users can store a graph of their network and use graph queries to answer questions like how many hosts are running a certain application. It can store and process billions of events to better manage and secure business networks and detect anomalies.
“For example, if you detect a malicious file on a host, Neptune can help you to find the connections between the hosts that spread the malicious file, and enable you to trace it to the original host that downloaded it,” the site noted.
Neptune can be used to build social networking applications thanks to its ability to quickly process large sets of user profiles and interactions. It can also run interactive graph queries with high throughput, so developers can more easily build social features into applications.
For example, when building a social feed into an app, Neptune can provide results that prioritize showing users the latest update from their family, from friends who live close by and from people whose updates they “like.”
Neptune allows developers to store relationships between information like customer interests, friends and purchase history into a graph and quickly query it to make personalized recommendations. For example, with Neptune, you can make product recommendations to a user based on others who like the same sport and have a similar purchase history.
Neptune allows developers to use relationships to process financial and purchase transactions in near real-time to more easily detect fraud patterns. It can execute fast graph queries to see if a customer is using the same email address and credit card as a known fraud case, according to the website.
SEE: Learn how Australia is combating financial scams with cross-banking collaboration.
With Neptune, developers can build knowledge graph applications that allow for storing information in a graph model and using graph queries to navigate those datasets. Open-source and open-standard application programming interfaces supported by the database can help you quickly use existing information resources to build your own knowledge graphs and host them on a fully managed service.
Applications built with Neptune can store and navigate life sciences information and process sensitive data using encryption at rest. For example, with Neptune, you can store models of disease and gene interactions and search for graph patterns to find other genes that may be associated with a given disease, the site noted. Users can also create and store patient relationships from medical records across different systems.
Amazon Neptune’s general availability presents a scalable and efficient solution for handling highly connected datasets. Its flexibility in application development makes it a versatile tool for many industries, with applications ranging from building social networks and recommendation engines to fraud detection, knowledge graphs and drug discovery applications.
Plus, its pay-as-you-go pricing and availability across several regions make it a strong option for clients who value cost-effectiveness. If you want to innovate and leverage complex data relationships, consider incorporating Neptune into your data strategy.
SEE: Check out our Amazon Web Services cheat sheet for everything AWS.
