Table of contents
Fully Managed Service
Amazon Neptune is a fully managed graph database service, which eliminates the need for database administration tasks such as hardware provisioning, patching, setup, configuration, backups, and scaling.
Supports Multiple Graph Models
Neptune supports both property graph and RDF graph models, utilizing popular graph query languages like Gremlin and SPARQL, providing flexibility for various use cases.
High Performance and Scalability
Designed for fast query execution and high throughput in complex graphs, Neptune can seamlessly scale to handle hundreds of billions of relationships and queries with low latency.
High Availability and Durability
Amazon Neptune is designed for high availability with read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across Availability Zones.
Integration with AWS Ecosystem
As a part of AWS, Neptune integrates well with other AWS services such as AWS Identity and Access Management (IAM), AWS Lambda, and Amazon CloudWatch for enhanced functionality and security.
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Check the traffic stats of Amazon Neptune on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Amazon Neptune on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Amazon Neptune's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Amazon Neptune on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Amazon Neptune on Reddit. This can help you find out how popualr the product is and what people think about it.
The key difference lies in the retrieval mechanism. Vector databases focus on semantic similarity by comparing numerical embeddings, while graph databases emphasize relations between entities. Two solutions for graph databases are Neptune from Amazon and Neo4j. In a case where you need a solution that can accommodate both vector and graph, Weaviate fits the bill. - Source: dev.to / about 1 year ago
This technical example was built upon an AWS AI service suite to test its capabilities, and it was pretty impressive, with minimal learning curve for the AI enthusiast. This example leverages Neptune as the graph database, Bedrockโs Claude v3 for our GenAI model and LLM, along with out-of-the-box security notebooks, to populate the data. This coupled with excellent docs and some tinkering helped wire the example... - Source: dev.to / over 2 years ago
Graph databases are designed to store and process highly connected data, such as social networks, recommendation engines, and fraud detection systems. AWS offers a fully managed graph database service called Amazon Neptune that can handle graph data at scale. - Source: dev.to / almost 3 years ago
My understanding is that a shard is the full set of services that are needed to support at least one game server, and so it isn't a shard that crashes, it's (usually) a "dynamic" game server (DGS) ( which there's currently only one of per shard until they build out the ~~replication layer~~ (Atlas service? https://sc-server-meshing.info/), so it feels an awful lot like the whole shard crashed )... But the DGS... Source: about 3 years ago
I know an alternative to regular SQL relational and noSQL databases is graph databases like Neo4j and Amazon Neptune. I don't know if it's relevant to you but you might want to check out https://en.m.wikipedia.org/wiki/Neo4j or https://aws.amazon.com/neptune/. Source: about 3 years ago
First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language. - Source: dev.to / over 3 years ago
What's your thought on AWS Neptune? From the marketing page below: "Scale your graphs with unlimited vertices and edges, and more than 100,000 queries per second for the most demanding applications. Storage scaling of up to 128Tib per cluster and read scaling with up to 15 replicas per cluster." https://aws.amazon.com/neptune/. - Source: Hacker News / over 3 years ago
I believe this is only the first step in Amazonโs plan to push the database further. With the rise of social networks and recommendation engines, graph databases have become more popular. Amazonโs new Neptune graph database is an foray into another data area. Graph databases are notoriously hard to shard, so it may be a while before we see a Serverless Neptune. I wouldnโt bet against it coming eventually. - Source: dev.to / about 4 years ago
I want to read IMDb Datasets and process the title.basics.tsv.gz so that I can play with Amazon Neptune. - Source: dev.to / over 4 years ago
Over time, we should have only dockers and lambda functions in our compute. While doing this, we should also discard the EC2 instances one by one and move onto Fargate. Drop the Kafka or other messaging services and move to Kinesis, EventBridge, SNS or SQS, as per the requirement. Migrate to cloud native databases like Aurora, DocumentDB, DynamoDB, and other purpose built databases like TimeStream, Keyspace,... - Source: dev.to / almost 5 years ago
As an AWS person, I became really interested in how I may take advantage of a an AppSync GraphQL API backed by a graph database. There are many great options to choose from, including things like Neo4j and ArangoDB which I hope to also try out sometime soon, but for this build I chose to use Amazon Neptune. - Source: dev.to / over 5 years ago
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