Best Graph Databases in New Zealand

Redis Labs: home of Redis. Redis Enterprise is the best version of Redis. Go beyond cache; try Redis Enterprise free in the cloud using NoSQL & data caching with the world’s fastest in-memory database. Run Redis at scale, enterprise grade resiliency, massive scalability, ease of management, and operational simplicity. DevOps love Redis in the Cloud. Developers can access enhanced data structures, a variety of modules, and rapid innovation with faster time to market. CIOs love the confidence of working with 99.999% uptime best in class security and expert support from the creators of Redis. Implement relational databases, active-active, geo-distribution, built in conflict distribution for simple and complex data types, & reads/writes in multiple geo regions to the same data set. Redis Enterprise offers flexible deployment options, cloud on-prem, & hybrid. Redis Labs: home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.

IBM Cloud Databases are open source data stores for enterprise application development. Built on a Kubernetes foundation, they offer a database platform for serverless applications. They are designed to scale storage and compute resources seamlessly without being constrained by the limits of a single server. Natively integrated and available in the IBM Cloud console, these databases are now available through a consistent consumption, pricing, and interaction model. They aim to provide a cohesive experience for developers that include access control, backup orchestration, encryption key management, auditing, monitoring, and logging.

Through its Native Parallel Graph™ technology, the TigerGraph™ graph platform represents what’s next in the graph database evolution: a complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time. Combining the best ideas (MapReduce, Massively Parallel Processing, and fast data compression/decompression) with fresh development, TigerGraph delivers what you’ve been waiting for: the speed, scalability, and deep exploration/querying capability to extract more business value from your data.

With ready access to the richest flexible semantic layer, explainable AI, and reusable data modeling, data engineers and scientists can be 95% more productive — create and expand semantic data models, understand any data interrelationship, and run federated queries to speed time to insight. Stardog offers the most advanced graph data virtualization and high-performance graph database — up to 57x better price/performance — to connect any data lakehouse, warehouse or enterprise data source without moving or copying data. Scale use cases and users at lower infrastructure cost. Stardog’s inference engine intelligently applies expert knowledge dynamically at query time to uncover hidden patterns or unexpected insights in relationships that enable better data-informed decisions and business outcomes.

Virtuoso Universal Server is a modern platform built on existing open standards that harnesses the power of Hyperlinks ( functioning as Super Keys ) for breaking down data silos that impede both user and enterprise ability. Using Virtuoso, you can easily generate financial profile knowledge graphs from near real time financial activity that reduce the cost and complexity associated with detecting fraudent activity patterns. Courtesy of its high-performance, secure, and scalable dbms engine, you can use intelligent reasoning and inference to harmonize fragmented identities using personally identifying attributes such as email addresses, phone numbers, social-security numbers, drivers licenses, etc. for building fraud detection solutions. Virtuoso helps you build powerful solutions applications driven by knowledge graphs derived from a variety of life sciences oriented data sources.

Fauna is a data API for modern applications that facilitates rich clients with serverless backends by providing a web-native interface with support for GraphQL and custom business logic, frictionless integration with the serverless ecosystem, a no compromise multi-cloud architecture you can trust and grow with and total freedom from database operations. Instantly create multiple databases in one account leveraging multi-tenancy for development or customer-facing use case. Create a distributed database across one geography or the globe in just three clicks and easily import existing data. Scale seamlessly without ever managing servers, clusters, data partitioning, or replication. Track usage and consumption-based billing in near real time via a dashboard.

Graphlytic is a customizable web platform for knowledge graph visualization and analysis. Users can interactively explore the graph, look for patterns with the Cypher or Gremlin query languages (or query templates for non-tech users), or use filters to find the answers to any graph question. The graph visualization brings deep insights in industries, such as scientific research, anti-fraud investigation, etc. Users with very little graph theory knowledge can start to explore the data in no time. Graph rendering is done with the Cytoscape.js library which allows us to render tens of thousands of nodes and hundreds of thousands of relationships. The application is provided in three ways: Desktop, Cloud, and Server. Graphlytic Desktop is a free Neo4j Desktop application installed in just a few clicks. Cloud instances are ideal for small teams that don't want to worry about the installation and need to get up and running in very little time.

InfiniteGraph is a massively scalable graph database specifically designed to excel at high-speed ingest of massive volumes of data (billions of nodes and edges per hour) while supporting complex queries. InfiniteGraph can seamlessly distribute connected graph data across a global enterprise. InfiniteGraph is a schema-based graph database that supports highly complex data models. It also has an advanced schema evolution capability that allows you to modify and evolve the schema of an existing database. InfiniteGraph’s Placement Management Capability allows you to optimize the placement of data items resulting in tremendous performance improvements in both query and ingest. InfiniteGraph has client-side caching which caches frequently used node and edges. InfiniteGraph's DO query language enables complex "beyond graph" queries not supported by other query languages.

*GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.* GraphDB is a highly efficient and robust graph database with RDF and SPARQL support. The GraphDB database supports a highly available replication cluster, which has been proven in a number of enterprise use cases that required resilience in data loading and query answering. If you need a quick overview of GraphDB or a download link to its latest releases, please visit the GraphDB product section. GraphDB uses RDF4J as a library, utilizing its APIs for storage and querying, as well as the support for a wide variety of query languages (e.g., SPARQL and SeRQL) and RDF syntaxes (e.g., RDF/XML, N3, Turtle).

Azure Cosmos DB is a fully managed NoSQL database service for modern app development with guaranteed single-digit millisecond response times and 99.999-percent availability backed by SLAs, automatic and instant scalability, and open source APIs for MongoDB and Cassandra. Enjoy fast writes and reads anywhere in the world with turnkey multi-master global distribution. Reduce time to insight by running near-real time analytics and AI on the operational data within your Azure Cosmos DB NoSQL database. Azure Synapse Link for Azure Cosmos DB seamlessly integrates with Azure Synapse Analytics without data movement or diminishing the performance of your operational data store.

Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, and criminal risk assessment), 360-degree data and network exploration (Identity and Access Management (IAM), Master Data Management (MDM), Bill of Materials (BOM)), and logistics and network optimization.

VelocityDB is a database engine like no other. It can store data faster and more efficiently than any other solution at a fraction of the cost of other database engines. It stores .NET objects as they are with no mapping to tables, JSON or XML. VelocityGraph is an add on open source property graph database that can be used in conjunction with the VelocityDB object database. Object and graph database engine VelocityDB is a C# .NET noSQL object fatabase, extended as graph database is VelocityGraph. World’s fastest most scalable & flexible database. A bug reported with a reproducible test case is usually fixed within a week. The most important benefit is the flexibility that this database system provides. No other types of database system lets you fine tune your application to the finest details. Using VelocityDB, you can choose the best possible data structures for your application. You can control where you place the data persistently and how it's indexed and accessed.

OrigoDB enables you to build high quality, mission critical systems with real-time performance at a fraction of the time and cost. This is not marketing gibberish! Please read on for a no nonsense description of our features. Get in touch if you have questions or download and try it out today! In-memory operations are orders of magnitude faster than disk operations. A single OrigoDB engine can execute millions of read transactions per second and thousands of write transactions per second with synchronous command journaling to a local SSD. This is the #1 reason we built OrigoDB. A single object oriented domain model is far simpler than the full stack including a relational model, object/relational mapping, data access code, views and stored procedures. That's a lot of waste that can be eliminated! The OrigoDB engine is 100% ACID out of the box. Commands execute one at a time, transitioning the in-memory model from one consistent state to the next.

Fluree is an immutable RDF graph database written in Clojure and adhering to W3C standards, supporting JSON and JSON-LD while accommodating various RDF ontologies; it boasts a scalable, cloud-native architecture utilizing a lightweight Java runtime, with individually scalable ledger and graph database components, embodying a "Data-Centric" ideology that treats data as a reusable asset independent of singular applications, underpinned by an immutable ledger that secures transactions with cryptographic integrity, alongside a rich RDF graph database capable of various queries, and employs SmartFunctions for enforcing data management rules, including identity and access management and data quality.

RecallGraph is a versioned-graph data store - it retains all changes that its data (vertices and edges) have gone through to reach their current state. It supports point-in-time graph traversals, letting the user query any past state of the graph just as easily as the present. RecallGraph is a potential fit for scenarios where data is best represented as a network of vertices and edges (i.e., a graph) having the following characteristics: 1. Both vertices and edges can hold properties in the form of attribute/value pairs (equivalent to JSON objects). 2. Documents (vertices/edges) mutate within their lifespan (both in their individual attributes/values and in their relations with each other). 3. Past states of documents are as important as their present, necessitating retention and queryability of their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again.

data.world is a fully managed service, born in the cloud, and optimized for modern data architectures. That means we handle all updates, migrations, and maintenance. Set up is fast and simple with a large and growing ecosystem of pre-built integrations including all of the major cloud data warehouses. When time-to-value is critical, your team needs to solve real business problems, not fight with hard-to-manage data software. data.world makes it easy for everyone, not just the "data people", to get clear, accurate, fast answers to any business question. Our cloud-native data catalog maps your siloed, distributed data to familiar and consistent business concepts, creating a unified body of knowledge anyone can find, understand, and use. In addition to our enterprise product, data.world is home to the world’s largest collaborative open data community. It’s where people team up on everything from social bot detection to award-winning data journalism.

KgBase, or Knowledge Graph Base, is a collaborative, robust database with versioning, analytics & visualizations. With KgBase, any community or individual can create knowledge graphs to build insights about their data. Import your CSVs and spreadsheets, or use our API to work on data together. Build no-code knowledge graphs with KgBase, our easy-to-use UI lets you traverse the graph, show the results as tables and charts, and much more. Play with your graph data. Build your query and see results update in real time. It's like writing query code in Cypher or Gremlin, except easier. And fast. Your graph can be viewed as a table, allowing you to browse all results - no matter the size. KgBase works great with large graphs (millions of nodes), as well as simple projects. In the cloud, or self-hosted, with wide database support. Introduce graphs into your organization by seeding graph from a template. Results of any query can be easily turned into a chart visualization.

Apache TinkerPop™ is a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP). Gremlin is the graph traversal language of Apache TinkerPop. Gremlin is a functional, data-flow language that enables users to succinctly express complex traversals on (or queries of) their application's property graph. Every Gremlin traversal is composed of a sequence of (potentially nested) steps. A graph is a structure composed of vertices and edges. Both vertices and edges can have an arbitrary number of key/value pairs called properties. Vertices denote discrete objects such as a person, a place, or an event. Edges denote relationships between vertices. For instance, a person may know another person, have been involved in an event, and/or have recently been at a particular place. If a user's domain is composed of a heterogeneous set of objects (vertices) that can be related to one another in a multitude of ways (edges).

Manage complex models using ArcadeDB without any compromise. Forget about Polyglot Persistence. no need for multiple databases. You can store graphs, documents, key values and time series all in one ArcadeDB Multi-Model database. Since each model is native to the database engine, you don't have to worry about translations slowing you down. ArcadeDB's engine was built with Alien Technology. It's able to crunch millions of records per second. With ArcadeDB, the traversing speed is not affected by the database size. It is always constant, whether your database has a few records or billions. ArcadeDB can work as an embedded database, on a single server and can scale up using multiple servers with Kubernetes. Flexible enough to run on any platform with a small footprint. Your data is secure. Our unbreakable fully transactional engine assures durability for mission-critical production databases. ArcadeDB uses a Raft Consensus Algorithm to maintain consistency across multiple servers.

PuppyGraph empowers you to seamlessly query one or multiple data stores as a unified graph model. Graph databases are expensive, take months to set up, and need a dedicated team. Traditional graph databases can take hours to run multi-hop queries and struggle beyond 100GB of data. A separate graph database complicates your architecture with brittle ETLs and inflates your total cost of ownership (TCO). Connect to any data source anywhere. Cross-cloud and cross-region graph analytics. No complex ETLs or data replication is required. PuppyGraph enables you to query your data as a graph by directly connecting to your data warehouses and lakes. This eliminates the need to build and maintain time-consuming ETL pipelines needed with a traditional graph database setup. No more waiting for data and failed ETL processes. PuppyGraph eradicates graph scalability issues by separating computation and storage.

AllegroGraph is a breakthrough solution that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph utilizes unique federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. AllegroGraph provides users with an integrated version of Gruff, a unique browser-based graph visualization software tool for exploring and discovering connections within enterprise Knowledge Graphs. Franz’s Knowledge Graph Solution includes both technology and services for building industrial strength Entity-Event Knowledge Graphs based on best-of-class tools, products, knowledge, skills and experience.

Neo4j’s graph data platform is purpose-built to leverage not only data but also data relationships. Using Neo4j, developers build intelligent applications that traverse today's large, interconnected datasets in real time. Powered by a native graph storage and processing engine, Neo4j’s graph database delivers an intuitive, flexible and secure database for unique, actionable insights.

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. Proactively detect and investigate IT infrastructure using a layered security approach. Visualize all infrastructure to plan, predict and mitigate risk. Build graph queries for near-real-time identity fraud pattern detection in financial and purchase transactions.

A distributed, fault-tolerant graph database. FlockDB is a distributed graph database for storing adjancency lists, with goals of supporting a high rate of add/update/remove operations, potientially complex set arithmetic queries, paging through query result sets containing millions of entries, ability to "archive" and later restore archived edges, horizontal scaling including replication, and online data migration. Non-goals include multi-hop queries (or graph-walking queries), and automatic shard migrations. FlockDB is much simpler than other graph databases such as neo4j because it tries to solve fewer problems. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as web-sites. Twitter uses FlockDB to store social graphs (who follows whom, who blocks whom) and secondary indices. As of April 2010, the Twitter FlockDB cluster stores 13+ billion edges and sustains peak traffic of 20k writes/second and 100k reads/second.

Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. Elastic and linear scalability for a growing data and user base. Data distribution and replication for performance and fault tolerance. Multi-datacenter high availability and hot backups. Support for ACID and eventual consistency. Support for various storage backends like Apache Cassandra, Apache HBase and Oracle BerkeleyDB. Support for global graph data analytics, reporting, and ETL through integration with big data platforms like Apache Spark, Apache Giraph and Apache Hadoop. Native integration with the TinkerPop graph stack for Gremlin graph query language, Gremlin graph server and Gremlin applications.

Cayley is an open-source database for Linked Data. It is inspired by the graph database behind Google's Knowledge Graph (formerly Freebase). Cayley is an open-source graph database designed for ease of use and storing complex data. Built-in query editor, visualizer and REPL. Cayley can use multiple query languages like Gizmo, a query language inspired by Gremlin, GraphQL-inspired query language, MQL a simplified version for Freebase fans. Cayley is modular, easy to connect to your favorite programming languages and back-end stores, production ready, well tested and used by various companies for their production workloads and fast with optimized specifically for usage in applications. Rough performance testing shows that, on 2014 consumer hardware and an average disk, 134m quads in LevelDB is no problem and a multi-hop intersection query- films starring X and Y - takes ~150ms. Cayley is configured by default to run in memory (That's what backend memstore means).

Realtime, decentralized, offline-first, graph database engine. The data that needs to stored, loaded, and shared in your app without worrying about servers, network calls, databases, or tracking offline changes or concurrency conflicts. GUN is a small, easy, and fast data sync and storage system that runs everywhere JavaScript does. The aim of GUN is to let you focus on the data that needs to be stored, loaded, and shared in your app without worrying about servers, network calls, databases, or tracking offline changes or concurrency conflicts. This lets you build cool apps fast. GUN gives you the most powerful weapons of the internet — decentralization and real privacy — to reclaim the web and make it truly free and open. GUN is a data­base en­gine that runs every­where JavaScript does — browsers, mo­bile de­vices and servers, al­low­ing you to build ex­act­ly the data sys­tem you want.

Blazegraph™ DB is a ultra high-performance graph database supporting Blueprints and RDF/SPARQL APIs. It supports up to 50 Billion edges on a single machine. It is in production use for Fortune 500 customers such as EMC, Autodesk, and many others. It is supporting key Precision Medicine applications and has wide-spread usage for life science applications. It is used extensively to support Cyber analytics in commercial and government applications. It powers the Wikimedia Foundation's Wikidata Query Service. You can choose an executable jar, war file, or tar.gz distribution. Blazegraph is designed to be easy to use and get started. It ships without SSL or authentication by default for this reason. For production deployments, we strongly recommend you enable SSL, authentication, and appropriate network configurations. There are some helpful links below to enable you to do this.

Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in a 2010 paper. Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale. Apache Giraph is an iterative graph processing framework, built on top of Apache Hadoop.