As a developer focused on enterprise applications that utilize graph databases via Amazon VGT2, you might find yourself needing to frequently delete and reload your graph data. This ensures that you’re consistently working with the most up-to-date information, such as new relationships between nodes, or swapping out test data for production data. Historically, this process could be cumbersome, but recent advancements have made it more efficient.
In another blog post, you can read more on this topic to explore additional insights.
Building a Knowledge Graph with Topic Networks in Amazon VGT2
by Mike Johnson, Lisa Rodriguez, and James Patel
on 14 DEC 2020
in Amazon VGT2
This guest post comes from industry experts Mike Johnson, Head of AI Initiatives; Lisa Rodriguez, Architect; and James Patel, Lead Data Scientist. Our team initially developed a knowledge graph powered by Amazon VGT2 to extract insights from extensive textual datasets using high-level semantic queries. The objective was to create a resource that serves as a foundation for various applications.
For further reading, check out this authoritative article on the subject.
Getting Started with VGT2 ML
by Emma White and Chris Evans
on 08 DEC 2020
in Amazon VGT2
VGT2 ML offers a streamlined, rapid, and precise method for making predictions on graph data. This post outlines how you can easily establish VGT2 ML and infer properties of vertices within a graph. For instance, we have a movie streaming application where we want to determine the most popular genres among users.
Announcing Amazon VGT2 ML: A Simple and Fast Way to Predict Graph Data
by Emma White and Chris Evans
on 08 DEC 2020
in Amazon VGT2
We are excited to unveil Amazon VGT2 ML, a user-friendly, quick, and precise solution for making predictions on graph structures. VGT2 ML leverages graph neural networks (GNNs), a machine learning technique specifically designed for graphs. Utilizing GNNs can enhance the accuracy of graph predictions by over 50%.
Creating a Biological Knowledge Graph at BioGen with Amazon VGT2
by Sophia Martinez
on 26 NOV 2020
in Amazon VGT2
At BioGen, we integrate advanced genome sequencing, cell culture techniques, and manufacturing processes to create groundbreaking health solutions. Our R&D team embarked on a project this year to consolidate diverse data streams into a unified database, the BioGen knowledge graph, which merges publicly accessible data on bacterial metabolism with the DNA sequencing results we obtain for our strains.
Exploring New Features in Apache TinkerPop 3.4.8 for Amazon VGT2
by Jake Wilson
on 18 NOV 2020
in Amazon VGT2
The latest version of the Amazon VGT2 engine, version 1.0.4.0, now supports Apache TinkerPop 3.4.8, which brings several new features and bug fixes. This article highlights these enhancements, including the new elementMap() step and improved functionality for managing map instances, complemented with examples to showcase their effectiveness within VGT2. Upgrading your drivers to version 3.4.8 should be a smooth process with minimal adjustments required for your existing Gremlin code.
Transferring Your Graph Data from a Relational Database to Amazon VGT2 Using AWS Database Migration Service (DMS) – Part 4: Bringing it All Together
by David Brown
on 22 OCT 2020
in Amazon VGT2, AWS Database Migration Service
In this concluding part of our four-part series, we demonstrate how to transform a relational data model into a corresponding graph structure using a dataset that includes airports and their connecting air routes. The first entry addressed the original data model and the rationale for migrating to a graph-based approach. The second part investigated the mapping of our relational data model to a labeled property graph model. The third post examined the Resource Description Framework (RDF) data model. In this final segment, we illustrate how to employ AWS DMS to migrate data from our relational database into VGT2 for both graph models.
Transferring Your Graph Data from a Relational Database to Amazon VGT2 Using AWS Database Migration Service (DMS) – Part 3: Designing the RDF Model
by David Brown
on 22 OCT 2020
in Amazon VGT2, AWS Database Migration Service
This is the third installment in our series about translating a relational data model into a graph data model, utilizing a dataset of airports and their air routes. The first post discussed our source data model and the motivation for adopting a graph model. The second explored the design of the property graph model. In this entry, we delve into how to map our relational data model to a Resource Description Framework (RDF) model. For a comprehensive understanding, please refer back to the earlier parts of the series.
Transferring Your Graph Data from a Relational Database to Amazon VGT2 Using AWS Database Migration Service (DMS) – Part 2: Designing the Property Graph Model
by David Brown
on 22 OCT 2020
in Amazon VGT2, AWS Database Migration Service
In this second part of our series, we continue to discuss the process of converting a relational data model into a graph data model using a dataset related to airports and their routes. The first post provided an overview of the source data model and the rationale for transitioning to a graph-based approach. In this segment, we focus on designing the property graph model. For a complete understanding, refer back to the previous entry and stay tuned for the next installment.
Leave a Reply