What is Amazon Neptune Analytics?
Amazon Neptune Analytics is a memory-optimized graph database engine for analytics. It keeps large graph datasets in memory so that you can process large amounts of graph data in seconds and surface insights and trends.
The service complements Amazon Neptune Database. For intensive analysis you load data from a Neptune Database graph or snapshot into Neptune Analytics; graph data stored in Amazon S3 can be loaded as well. Per the documentation, Neptune Analytics is an ideal choice for investigatory, exploratory, or data-science workloads that require fast iteration, analytical and algorithmic processing, or vector search on graph data.
Core Features
- In-memory analysis: Large graphs are loaded into memory quickly and analyzed there
- Graph algorithms: A library of optimized analytic algorithms; per the product page these include path finding, community detection, centrality, and similarity
- openCypher: Pre-built and custom queries in an established graph query language
- Vector search: Vector search within graph traversals, for example in retrieval scenarios using embeddings
- Graph as a service: AWS manages graphs instead of infrastructure and provisions compute resources automatically based on graph size
- Flexible data sources: Load from Neptune Database (graph or snapshot) and from Amazon S3
- Scale: Per the documentation, you can analyze graphs with tens of billions of relationships and process thousands of analytic queries per second
Typical Use Cases
Fraud investigations: Analyze relationships between accounts, devices, and transactions to make suspicious patterns visible.
Network threat detection: Analyze relationships within security environments, for example to assess attack paths and dependencies.
Recommendations: Compute targeted content and recommendations inside data science workflows.
Analysis without a custom pipeline: Per the documentation, providing a simple API for loading, querying, and analyzing graph data removes the overhead of building and managing complex data-analytics pipelines.
Benefits
- Analysis of large graphs in seconds instead of long batch runs
- Pre-built algorithms complement your own openCypher queries
- Vector search and graph traversal in a single engine
- Works with data from Neptune Database or from a data lake in Amazon S3
- Billing based on actual runtime, with a pause capability for idle periods
Integration with innFactory
As an AWS Reseller, innFactory supports you with Amazon Neptune Analytics: modeling and importing your graph data from Neptune Database or Amazon S3, selecting suitable graph algorithms for your question, building vector search scenarios on graph data, and sizing capacity with runtime and pause strategies for cost control.
Typical Use Cases
Technical Specifications
Frequently Asked Questions
What is Amazon Neptune Analytics?
Per the documentation, Neptune Analytics is a memory-optimized graph database engine for analytics. It stores large graph datasets in memory so that large amounts of graph data can be processed in seconds. It supports a library of optimized graph analytic algorithms, low-latency graph queries, and vector search capabilities within graph traversals.
How does Neptune Analytics differ from Neptune Database?
Per the documentation, Neptune Database is a serverless graph database designed for scalability and availability — up to 100,000 queries per second, Multi-AZ high availability, and multi-Region deployments, for workloads such as social networking, fraud alerting, and Customer 360 applications. Neptune Analytics, by contrast, is an analytics engine for quickly analyzing existing graph databases or graph datasets stored in a data lake.
How does data get into Neptune Analytics?
Per the documentation, you load data from a Neptune Database graph or from a snapshot into Neptune Analytics. You can also load graph data that is stored in Amazon S3.
Which query language is used?
Per the documentation, Neptune Analytics uses openCypher. You use it both for your own analytical queries and for the pre-built graph algorithms.
How is Neptune Analytics billed?
Per the pricing page, billing is based on m-NCUs (memory Neptune Capacity Units); one m-NCU corresponds to one GB of memory plus corresponding compute and networking capacity for one hour. You can terminate a graph as soon as the analysis is done and are billed only for the duration it was running. For idle periods the pricing page describes a pause capability at 10% of normal compute pricing. Backup storage and data transfer are charged in addition.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of AWS (official documentation). This page does not represent an offer by AWS.