GraphNews

In the 4th part of this series of posts we look at a different way of data modeling for APIs, one that is based on Linked Data standards. First, some

I've been meaning to revisit some of the things i've been writing about and getting feedback on lately - APIs, the JSON vs. XML "non" debate and Linked

Defining semantics is a matter of semantics, not less so in the Big Data space.

Data integration may not sound as deliciously intriguing as AI or machine learning tidbits sprinkled on vanilla apps. Still, it is the bread and butter of many, the enabler of all things cool using data, and a premium use case for concepts underpinning AI.

Creating winning analytics solutions means combining and making the most of different approaches and techniques. Taking a look at how Google does this for YouTube can provide inspiration and set a framework for analytics solutions.

In a rapidly shifting job market, being able to formalize skills is a requirement for job seekers and employers going forward. Open Badges is a data specification that may be able to help do that.

Notepads, graphs, data lakes, collaboration, and data manifestos. Data.world has an interesting blend of philosophy and technology going on -- and it all converges around one thing: Facilitating data-driven analysis by making it a team sport.

Did you ever need data on a topic you wanted to research, and had a hard time finding it? Wish you could just Google it? Well, now you can do that.

Yes, Facebook is a data-driven monopoly. But the only real way to break it up is by getting hold of its data and functionality, one piece at a time. It will take a combination of tech, data, and social engineering to get there. And graphs -- personal knowledge graphs.

Being disrupted by Big Tech is one of the greatest concerns for any business. Good news: There may be a path to accelerate digital transformation and out-compete Big Tech, by leveraging domain knowledge.

You don't have to be a fortune teller to identify AI as the key trend for the 2020s. But there is nuance regarding AI hardware and software that deserves to be highlighted.

Data.world wants to eliminate data silos to answer business questions. Their bet to do this is to provide data catalogs powered by knowledge graphs and semantics. The choice of technology seems to hit the mark, but intangibles matter, too.

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?

DataStax Enterprise just released version 6.0, a major upgrade. We take an insider tour with Jonathan Lacefield, Senior Director of Product Management

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?

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 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.

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.

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.

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.

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.

Graph databases have been the hottest area of data management for a while now. Today, this landscape shifts once again in the last few months.

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.

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.

Siren promises the benefits of knowledge graphs, without the hassle of reinventing your existing data management and infrastructure

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

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.

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.