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Reflections on the 2022 Data & AI Summit
A trip report reflecting on the highlights of the 2022 Data & AI Summit. We discuss Spark Connect, Delta Lake 2.0, Databricks Unity Data Catalog, and Partner Connect. Databricks has mapped out a path to the future, and Stardog is well-aligned with our partner Databricks’ worldview.
Graph Databases vs. Relational Databases: Know the Differences
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.
Working with Messy Data: You May Have More Flexibility Than You Think
For a knowledge graph to be useful, the data must be valid and consistent. However, concerns about messy data should not impede one from moving forward with a knowledge graph project.
Introducing Stardog 8.0
Introducing the next major release of our Enterprise Knowledge Graph platform - Stardog 8.0, which contains new innovations to streamline data exploration and discovery for all citizen data users. Stardog 8.0’s new capabilities eliminate the need for specialized skills when querying a reusable, flexible semantic data layer, making it easier than ever before to accelerate data-driven insights across the enterprise.
Round-up: Knowledge Graph Conference 2022
KGC 2022 is in the books. Read about Stardog’s SVP of Engineering Mike Grove’s experience and conference presentation.
3 Ways Knowledge Graphs Can Fuel Big Data Analytics in Automotive, Now
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.
Why X-Tech Startups are Seizing on Knowledge Graph
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 and Machine Learning
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.
Announcing Stardog 7.9.0
I’m excited to introduce Stardog 7.9.0, which contains exciting new capabilities and features to help companies bring data together, identify new insights, and enable data-informed decisions. The main attraction of this release is our new Stardog Designer application, a no-code, visual environment to help rapidly engineer knowledge graphs to drive down the time and cost of generating insights based on cross-domain data.
Introducing Stardog Designer
Announcing Stardog Designer, a new, visual environment for creating and maintaining your Knowledge Graph. Designer is an application for no-code, visual modeling and data source mapping. Designer makes implementing a knowledge graph much more intuitive, especially for those new to graph databases.
How to Build a Knowledge Graph
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.
Benchmarking Needs for Enterprise Knowledge Graph Excellence (and Awe)
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.