Stardog v8.2.0 Is Here!
Navin Sharma
Dec 7, 2022, 3 minute read

We are excited for you to experience the latest innovations in the Stardog v8.2.0 release. This release contains several new features and many improvements that close the gap between data and insights.

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Knowledge Graphs: Make This Smart Shift to Your Data & Analytics Approach
Natalie ClarkColleen Luther
Nov 29, 2022, 5 minute read

Are you managing data and analytics at your organization? Learn how tech leaders are using knowledge graphs to transform their approach.

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Where Does Business Logic Go?
Kendall Clark
Nov 11, 2022, 9 minute read

Let’s talk about business logic. Where should it live inside the enterprise?

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Semantic Layers: Four Characteristics that Define the Valuable New Approach
Kendall Clark
Nov 11, 2022, 11 minute read

The secret to data analytics success today is a democratized, self-service data platform, and that’s only possible when you leverage a semantic layer to shift from columns to concepts.

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Activate Metadata as Part of your Semantic Layer with Stardog's Integration of Databricks Unity Catalog
Navin Sharma
Oct 28, 2022, 5 minute read

Modern enterprises need to develop a Data Governance Framework. For effective governance, this is generally underpinned by a catalog megastore. Unity Catalog provides that megastore across the lakehouse, enabling business users access to full metadata across the workspaces and allowing them to visualize the relationships across the data landscape. The creation of a knowledge layer is accelerated when this metadata is available for modeling in Stardog platform.

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Graph Database Examples
Sep 28, 2022, 9 minute read

How do companies use graph databases in real life? At Stardog, our platform combines graph technology with semantics to enable knowledge graphs. Consider the ability to deeply understand and work with connections between organizations, people, transactions, and events (to name a few examples). Add in inferencing, scalability, and near real-time traversal of big data. The number of graph use cases explodes. Relational databases like SQL simply can’t deliver like graph can.

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Thoughts From Our 2022 Gartner Data & Analytics Summit Experience
Colleen Luther
Sep 12, 2022, 5 minute read

“Enterprises are heading not toward a “new normal” but rather a “no normal.” This theme, chosen by Gartner, reinforces many of the positions held by Stardog. We’ll give you a quick summary of the conference and our takeaways here.

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Announcing Stardog 8.1
Navin Sharma
Sep 7, 2022, 3 minute read

Stardog 8.1.0 is now available! Read on to see what’s improved in Stardog Designer, Stardog Cloud, and the platform in general.

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Stardog Is Now Available in Databricks Partner Connect
Navin Sharma
Aug 24, 2022, 6 minute read

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.

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Common Graph Databases Use Cases
Aug 5, 2022, 7 minute read

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.

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Reflections on the 2022 Data & AI Summit
Kendall Clark
Jul 19, 2022, 5 minute read

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.

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Graph Databases vs. Relational Databases: Know the Differences
Jul 12, 2022, 7 minute read

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.

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