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The Databricks Partner Connect integration makes it faster and easier for Databricks users. Get started on a path toward building a semantic data layer on top of the Databricks Lakehouse Platform. As an existing Databricks user, you can launch a new instance of Stardog Cloud using your Databricks credentials.
There are many reasons for data and analytics leaders to embrace graph techniques. These techniques can provide unique insight into business problems, especially those problems requiring contextual awareness of the connections and disconnections between multiple entities, including organizations, people, transactions, and events. A well-designed and richly populated graph can capture essential data relationships and their variable nature.
Graph refers to a data organization system that emphasizes the relationships between data points. This approach contrasts with the more traditional relational data systems — where data is stored in tables.
What is the most disruptive force in the automotive industry? Electric engines? New business models? Consumer mobility? Nope. The real firebrand for the industry lies in a different kind of engine known as data. Automakers, suppliers, and dealers must adapt quickly to the changing landscape by harnessing the power of data and knowledge graphs to jump-start advanced analytics today.
The phrase “every company is a tech company” is giving way to “every company is a data company,” which I believe means the value of technology is shifting from applications to data. And as the size and complexity of data multiply, organizations are increasingly turning to semantic knowledge graphs to harness data and unleash insights that fuel data-informed decisions.
Knowledge graphs make it easier to feed better and richer data into ML algorithms. The inherent traits of knowledge graphs posit them as a top tool of modern AI and ML strategy. Let’s examine a few ways in which they help.
Practical steps for building knowledge graphs: powerful tools for linked data, data integration, and data management. Scale all those use cases that have been inspired by data science. Increase your number of users, as needed. And spread the use of data itself. Do you really need a knowledge graph? Data rules the world. But organizations struggle to leverage that data for a competitive advantage. Raw, uninterpreted data in a system somewhere isn’t very helpful.
The tricky task of benchmarking Enterprise Knowledge Graphs—what potential clients ask for, what factors affect benchmarking, and more. A Q&A with Stardog’s CTO, Evren Sirin.
We hear from many of our customers that they’re interested in building a data fabric, but they’re not sure how to get started. Luckily, with a knowledge graph-based approach, you can start small and grow your data fabric over time. In this post, we’ll share an easy, approachable 5-step process for getting started with a data fabric.
Visualizing your data is a great way to eyeball your analysis and share the results with colleagues. Stardog is making this easier with a new feature in Studio: Stardog Charts.
Our latest benchmark report proves a 1 trillion-triple knowledge graph consisting of materialized data and Virtual Graphs spanning hybrid multicloud data sources.
Learn how IDB built FindIt, a semantic search platform that brings knowledge generated by the IDB Group to its staff and external audiences.