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Amazon Neptune releases Streams, SPARQL federated query for graphs and more | AWS Database Blog
Amazon Neptune releases Streams, SPARQL federated query for graphs and more | AWS Database Blog
The latest Amazon Neptune release brings together a host of capabilities that enhance developer productivity with graphs. This post summarizes the key features we have rolled out and pointers for more details. Getting started This new engine release will not be automatically applied to your existing cluster. You can choose to upgrade an existing cluster […]
·aws.amazon.com·
Amazon Neptune releases Streams, SPARQL federated query for graphs and more | AWS Database Blog
Amazon Neptune Workbench provides in-console experience to query your graph
Amazon Neptune Workbench provides in-console experience to query your graph
#Amazon Neptune #graphDB now offers a workbench, an in-console experience to query your graph. It lets users query Neptune w #Jupyter #notebooks using Gremlin or SPARQL #datascience #cloud #knowledgegreaph #AI #database AWSreinvent2019
·aws.amazon.com·
Amazon Neptune Workbench provides in-console experience to query your graph
An introduction to ontology engineering book
An introduction to ontology engineering book
This first general textbook An introduction to ontology engineering has as main aim to provide the reader with a comprehensive introductory overview of...
·linkedin.com·
An introduction to ontology engineering book
Andrei Kashcha on Twitter
Andrei Kashcha on Twitter
https://t.co/7T0EOs6yG7 - Made this tiny tool to discover related subreddits.The graph is created based on jaccard similarity between two subreddits. Jaccard similarity is constructed from set of shared users.Source code https://t.co/J9r1jl1JjR pic.twitter.com/4hcg7mI4sg— Andrei Kashcha (@anvaka) January 10, 2019
·twitter.com·
Andrei Kashcha on Twitter
Announcing Neo4j for Graph Data Science
Announcing Neo4j for Graph Data Science
grade features and scale. We appreciate your candid stories and collaboration, and we’ve used this to create a better solution. As such, we’re excited to announce Neo4j for Graph Data Science™, the first data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science is an ecosystem of tools that includes: With Neo4j for Graph Data Science, data scientists are empowered to confide
·neo4j.com·
Announcing Neo4j for Graph Data Science
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
We are super excited to share the latest upgrades to ArangoDB which are now available with ArangoDB 3.5. With the fast-growing team, we could build many new and long-awaited features in the open-source edition and Enterprise Edition. Get ArangoDB 3.5 on our download page and see all changes in the Changelog. Need to know more […]
·arangodb.com·
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB
Phew, it’s been quite a ride, but today the whole team is super excited to announce a $10 million Series A funding for ArangoDB, our native multi-model database. We feel honored and frankly a bit proud that Bow Capital is leading this investment round and shows their trust in our product, team, and amazing community. […]
·arangodb.com·
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB
AWS Data Migration Service now supports copying graph data from relational sources to Amazon Neptune
AWS Data Migration Service now supports copying graph data from relational sources to Amazon Neptune
AWS Data Migration Service (DMS) now supports migrating graph data from relational sources to Amazon Neptune. The AWS Database Migration Service (AWS DMS) enables you to migrate data from one data source to another. Using relational databases as source and Neptune as destination allows customers to copy their connected data into Neptune for graph queries.   Customers using both relational and graph databases today manually load their data into Neptune. DMS minimizes the manual effort to carry out the workflow. Using DMS, you can configure a destination endpoint to existing Neptune databases. DMS will carry out a full copy of data from relational databases to Neptune. The DMS workflow allows you to target either a RDF model or a property graph model by specifying the appropriate mapping file for each data model. DMS version 3.3.2 supports Amazon Neptune as the destination endpoint. You can configure DMS using the AWS Management Console, AWS SDK or CLI. You will be ch
·aws.amazon.com·
AWS Data Migration Service now supports copying graph data from relational sources to Amazon Neptune
Beam: A Distributed Knowledge Graph Store
Beam: A Distributed Knowledge Graph Store
We're excited to announce the public release of Akutan, a distributed knowledge graph store, under the Apache 2.0 open source license. Akutan is the result of four person-years of exploration and engineering effort, so there's a lot to unpack here! This post will discuss what Akutan is, how it's implemented, and why we've chosen to release it as open source.
·ebayinc.com·
Beam: A Distributed Knowledge Graph Store
Benefitting from SPARQL 1.1 Federated Queries with Amazon Neptune
Benefitting from SPARQL 1.1 Federated Queries with Amazon Neptune
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. Neptune supports the W3C’s graph model RDF, and its query language SPARQL. SPARQL 1.1 Federated Query specifies an extension to SPARQL for running queries distributed over different SPARQL endpoints.
·aws.amazon.com·
Benefitting from SPARQL 1.1 Federated Queries with Amazon Neptune