Category: Amazon Neptune

In a previous post, we described how to connect Amazon Neptune to metaphactory, securely, and then how to explore and search the Neptune graph data using metaphactory. In this post, we show how you can use metaphactory to build an end user application using its dynamic model driven components, driven by SPARQL queries.

Amazon Neptune 1.3.2.0 now supports the Apache TinkerPop 3.7.x release line, introducing many major new features and improvements. In this post, we highlight the features that have the greatest impact on Gremlin developers using Neptune, to help you understand the implications of upgrading to these versions of Neptune and TinkerPop.

In this post, I build upon the approach of the previous post and show how you can use TinkerGraph to unit test your transactional workloads. Additionally, I show how to use TinkerGraph in embedded mode. Embedded mode requires the use of Java, but it simplifies the test environment considerably as there is no need to run the server as a separate process.

We often want to take an inventory of the types of data in our database. What is our schema? This is most useful in DEV or TEST databases whose content is created by several users or teams, is often experimental, or has multiple versions. Even in controlled environments like PROD, where the application validates data […]

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this […]

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this […]

A joint letter to graph customers, the graph curious, and the Cypher community: Last week, the database world reached a significant milestone. The International Organization for Standardization (ISO) published GQL, a new database language standard designed for property graphs. GQL, which stands for Graph Query Language, is the first new ISO database language since the […]

It’s common in an enterprise for data that logically fits together to be separated into different databases. Some data is better suited for one storage than another, and it may not be feasible to locate all your data in one data store. But this data often needs to be linked back together to provide a […]

Knowledge graphs consolidate and integrate an organization’s information assets and make them more readily available to all members of the organization. There are many applications and use cases that are enabled by knowledge graphs. Graphs are a natural way to model and represent information about the world. This idea is not new, but has now […]

As organizations adopt modern application architectures such as microservices, application teams tend to retrofit one-size-fits-all databases. The mortgage industry is going through unprecedented transformation due to changing generation technologies such as API adoption. In the mortgage industry, API-enabled software allows lenders, issuers, borrowers, and more to integrate different functionalities into their portal, meaning they bring […]