Blog > Introduction to Amazon Neptune – Graph Database
Highly connected data is essential for many of today’s applications, including knowledge graphs, identity graphs, fraud graphs, social networking, and recommendation engines. Corresponding data needs to be managed and queried in a simple and fast way. But traditional databases are too rigid, and existing graph databases are difficult to scale as applications grow. Here we are discussing Amazon Neptune – Graph Database.
Amazon Neptune
Amazon Neptune is a fast, reliable, fully-managed graph database service that helps to build and run applications that work with highly connected data sets. The core of Amazon Neptune is a purpose-built graph database engine.
It is optimized for storing billions of relationships and querying the graph with millisecond latency. It supports popular graph models, property graph, W3C RDF, and their respective query languages Apache, tinkerpop, and sparkle. So, it’s easy to build queries that are efficiently navigating, highly connected data sets.
Neptune ML Capability
It also uses the amazon Neptune ML capability to utilize graph neural networks. A machine learning technique purpose-built to make easy and fast predictions using graph data. Neptune ML improves the accuracy of most predictions by over 50%; when compared to non-graph methods.
Low Latency: Amazon Neptune supports low latency read replicas across three availability zones.
Scalability: The user can easily scale their database deployment up and down as their needs change.
Availability & Durability: It is highly available, durable, and compliant, designed to provide greater than 99.99 percent availability. It features fault-tolerant and self-healing storage created for the cloud, which creates up to six copies of data in three different accessibility zones. In addition, it constantly backs up data to the Amazon S3 service and transparently recovers lost data during a disaster.
Security: It allows for multi-level data protection and access to them; with the help of network isolation and virtual private cloud network (VPC) and the possibility of encryption data in rest (using the AWS KMS service).
Pay-Per-Use: Amazon Neptune is a service billed in the pay-per-use model, i.e., payments only for the resources used. This allows its users to free themself from the unnecessary startup costs and complexity of planning the purchase of database capacity in advance.
Performance: Applications can scale out read traffic across up to 15 read replicas.
Where to use Amazon Neptune?
An organization can use the Amazon Neptune database in applications made for:
- E-commerce recommendation Engine
- Fraud Detection
- Building Knowledge Charts
- Network Security Analysis
Social Networking
Amazon Neptune also allows its users to process large interaction sets to create social applications quickly and easily. Its functionalities also help to prioritize the order of updates displayed to users.
Supports for Open Graph APIs
Amazon Neptune supports tools such as Gremlin or SPARQL while allowing the selection of the chart model. Properties and language of open-source queries while ensuring high efficiency of their operation.
Recommendation Engines
Amazon Neptune also allows users to use the highly available database more efficiently to create product recommendations. Recommendations can be based on a comparison of such as similar shopping histories among users or mutual friends.
Knowledge Graph
Education is another area where an organization can apply this database model. Using knowledge charts, the user can easily update information or expand and check complex regulatory rules models. An example is the Wikidata portal.
Life Science
With the Amazon Neptune database, users can store data such as disease models and genetic patterns. It helps to easily model relationships and chemical reactions that can be used in scientific publications.
Network and IT Operations
Moreover, it gives the ability t store and process events to manage and secure the network. Using this service, the user can easily understand how an anomaly can affect the network.
Author: SVCIT Editorial Copyright
Silicon Valley Cloud IT, LLC.