Stardog in the News

From Data Warehouses and Data Lakes to Data Fabrics for Analytics

Data fabrics are the cornerstone of modern analytics architecture, especially when implemented with data virtualization, knowledge graphs, and expressive data models.

How data fabrics are disrupting the analytics world

When properly implemented, data fabrics are a key technology area in artificial intelligence (AI), an ideal means of preparing data for machine learning, and an irreplaceable component in modern data integration methods that deliver better, faster and less expensive analytics than almost any other approach. The closer we can bring the common-sense business terminology and questions as the underlying fabric of data in an IT ecosystem, the more agility the enterprise builds to establish trust with highly connected and context-rich data to assist customers along every step of their journey.

Using a Semantic Layer to Propel a Data-Driven Culture

Data warehouses, data lakes, and data lakehouses are arguably the most popular methods of integrating data today. They’re older, current, and newer integration approaches—respectively—for running analytics and applications across all enterprise data. Using a semantic layer to connect them to the rest of the data ecosystem in the cloud, on-premises, and at the edge ensures their continued relevance now and in the future.

How Knowledge Graphs Can Deliver Insights When Integrating Decentralised Data

The majority of large data integration projects falter because systems are developed in a bespoke fashion, leaving organisations with critical applications that have been modified and transformed into a hornet’s nest of code that they cannot unravel for fear of breaking something. So, instead of trying to unravel systems, organisations have tended to duplicate processes, leading to more silos and custom solutions, which is an expensive proposition. Similarly, projects that end up as mass data cleansing and manipulation exercises are not sustainable in a world where organisations want insight into their data at speed.

Setting the Record Straight: Knowledge Graphs vs. Graph Databases

Although it might not be immediately discernible with all the marketing hype occluding this space, there are a number of pronounced distinctions between a true knowledge graph and a graph database.

2022 Trends in Data Modeling: The Interoperability Opportunity

The big data ecosystem is constantly expanding, gravitating ever further from the four walls of the traditional centralized enterprise with a burgeoning array of external sources, services, and systems. Capitalizing on this phenomenon requires horizontal visibility into data’s import for singular use cases—whether building predictive models, adhering to regulatory accords, devising comprehensive customer views and more—across a sundry of platforms, tools, and techniques.

Modern Data Integration Requires Hybrid Multicloud Data Fabric

What if data really is the fuel that powers engines of insight? Imagine a world exactly like ours in every way except for one key difference. Instead of the modern fossil fuel supply chain (i.e., Big Oil) being created before (or roughly coincident with) the mass production of automobiles (i.e., Ford, Volkswagen, etc.), the technology underlying actual race cars was perfected first.