Amazon Neptune features

With Neptune Database, you can increase read throughput to support high-volume application requests by creating up to 15 database read replicas. Neptune replicas share the same underlying storage as the source instance, lowering costs and avoiding the need to perform writes at the replica nodes. This frees up more processing power to serve read requests and reduces the replica lag time—often down to single-digit milliseconds. Neptune also provides a single endpoint for read queries so the application can connect without having to keep track of replicas as they are added and removed.

Neptune supports quick, efficient cloning operations, where entire multiterabyte database clusters can be cloned in minutes. Cloning is useful for a number of purposes including application development, testing, database updates, and running analytical queries. Immediate availability of data can significantly accelerate your software development and upgrade projects and make analytics more accurate.

You can clone a Neptune database with just a few steps in the console, without impacting the production environment. The clone is distributed and replicated across three AZs.

Neptune ML  is powered by SageMaker, which uses GNNs, an ML technique purpose-built for graphs, to make fast and more accurate predictions using graph data. With Neptune ML, you can improve the accuracy of most predictions for graphs by over 50% when compared to making predictions using nongraph methods.

Making accurate predictions on graphs with billions of relationships can be difficult and time-consuming. Existing ML approaches such as XGBoost can’t operate effectively on graphs because they are designed for tabular data. As a result, using these methods on graphs can take time, require specialized skills from developers, and produce suboptimal predictions.

Vector search makes it easy for you to build ML-augmented search experiences and generative AI applications. Use vector search if you want to build generative AI applications that combine data in an application domain and similarity search on vector embeddings. Vector search over graph data gives you an overall lower total cost of ownership and simpler management overhead because you do not need to manage separate data stores, build pipelines, or worry about keeping the data stores in sync.

Customers building generative AI applications can use vector search to augment their large language models (LLMs) by integrating graph queries for domain-specific context with the results from low-latency, nearest-neighbor similarity search on embeddings imported from LLMs hosted in Amazon Bedrock, GNNs in GraphStorm, or other sources. Neptune is integrated with LangChain, an open-source Python framework that makes it easier to develop generative AI applications using LLMs.

Neptune Analytics supports algorithms for path finding, detecting communities (clustering), identifying important data (centrality), and quantifying similarity. Path finding algorithms efficiently determine the shortest or most optimal route between two nodes. Path finding algorithms allow you to model real-world situations, such as road networks or social networks, as interconnected nodes and edges. Finding the shortest or most optimal paths between various points is crucial in applications such as route planning for GPS systems, logistics optimization, and even in solving complex problems in fields like biology or engineering.

Community detection algorithms calculate meaningful groups or clusters of nodes within a network, revealing hidden patterns and structures that can provide insights into the organization and dynamics of complex systems. This is valuable in fields such as social network analysis, biology (for identifying functional modules in protein-protein interaction networks), and even in understanding information flow and influence propagation in various domains.

Centrality algorithms help identify the most influential or important nodes within a network, providing insights into key players or critical points of interaction. This is valuable in fields such as social network analysis, where it helps pinpoint influential individuals, or in transportation networks, where it aids in identifying crucial hubs for efficient routing and resource allocation.

Graph similarity algorithms allow you to compare and analyze the structural similarities or dissimilarities between different graph structures, enabling insights into relationships, patterns, and commonalities across diverse datasets. This is invaluable in various fields such as biology (for comparing molecular structures), social networks (for identifying similar communities), and recommendation systems (for suggesting similar items based on user preferences).