RDF on Hadoop and Schema on Read vs. Schema on Write - Data Lakes & Warehouses
One of the challenges for any Big Data solution is dealing with scale, and RDF stores are no exception: going for billions of RDF triples (the equivalent
Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and opinions from Cloudera, DataStax, and IBM.
RDF is a graph data model you've probably either never heard of, or already dismissed. Why is that, could there be value in it, and how does it differ from the most popular graph data model out there?
AWS Neptune going GA: The good, the bad, and the ugly for graph database users and vendors
It's official: AWS has a production-ready graph database. What features are included today, and what will be included in the near future, what use cases are targeted, and what does AWS Neptune's release mean for users and graph database vendors?
Open or closed? On graph database access, query languages, community building, and TigerGraph - Graph Databases
Having an entry path, as well as a strong community, is important for any solution, and graph databases are no different. Following latest developments in
GraphQL for databases: A layer for universal database access?
GraphQL is a query language mostly used to streamline access to REST APIs. Now, a new breed of GraphQL implementations wants to build an abstraction layer for any database on top of GraphQL, and it seems to be catching up.
Knowledge graphs beyond the hype: Getting knowledge in and out of graphs and databases
What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.
Graph data standardization: It's just a graph, making gravitational waves in the real world
AWS, Google, Neo4j, Oracle. These were just some of the vendors represented in the W3C workshop on web standardization for graph data, and what transcribed is bound to boost adoption of the hottest segment in data management: Graph.
Advancing human exploration: Is space the final frontier, and how can data and AI get us there?
Fifty years after the moon landing, it's not just NASA working on what many consider the final frontier for humanity: space travel. NASA, however, is special, and one of the reasons is that data is at the heart of what it does.
Graph database reinvented: Dgraph secures $11.5M to pursue its unique and opinionated path
Imagine a graph database that's not aimed at the growing graph database market, selling to Fortune 500 without sales, and claiming to be the fastest without benchmarks. Dgraph is unique in some interesting ways.
Redis Labs goes Google Cloud, Graph, and other interesting places
Redis becomes available on Google Cloud Platform today, and we take the opportunity to explore its graph-shaped future, its community, and its licensing issues.
Graph for the mass market: Neo4j launches Aura on Google Cloud
Neo4j just launched its cloud offering, which it says will make graph mainstream. We discuss the offering with CEO Emil Eifrem, and take a look at where the graph market is right now.
Knowledge graph evolution: Platforms that speak your language
Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages
Neo4j 4.0 adds enterprise Fabric to its graph database
In its new release, Neo4j addresses key concerns for enterprise adoption. Scalability, security, management and architectural changes are here. And so is a strange feeling of deja-vu, too.
Make Apache Cassandra great again: DataStax going cloud, Kubernetes, open source, and multi-model
Actions and words, code and advocacy. DataStax is changing strategy, re-engaging with the Apache Cassandra open source community, and releasing some interesting technical advancements while at it, too.
Graph analytics and knowledge graphs facilitate scientific research for COVID-19
State of the art in analytics and AI can help address some of the most pressing issues in scientific research. Here is how top scientists are using them to facilitate coronavirus research.
Graph, machine learning, hype, and beyond: ArangoDB open source multi-model database releases version 3.7
A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.
Fluree, the graph database with blockchain inside, goes open source
A hitherto under the radar graph database that uses blockchain to support data lineage and verification wants to take over the world, starting with the US Department of Defense
Semantic data lake architecture in healthcare and beyond
Data lakes can be a great asset, but they need an array of elements to work properly. We take a look at how it works for Montefiore Health System and discuss the role of semantics and graph databases in the data lake architecture.
NBA analytics and RDF graphs: Game, data, and metadata evolution, and Occam's razor
Three-point shooting, Steph Curry, and coming up with stories. If you feel like doing your own analysis to investigate hypotheses or discover insights at any level, RDF graph's got your back. Case in point: The NBA.
2019 will be another 'Year of the Graph': OpenCorporates is evidence No. 1
Graph databases are crossing the chasm to mainstream use cases, adding features such as machine learning to their arsenal and becoming more cloud and developer friendly. Last year was a breakout year, and graph database growth and evolution is well under way in 2019.