With the growth of Azure, we're seeing more demand from long-time Microsoft customers who have made the move. While the Azure team is making heavy investments and improvements, there are still pitfalls and gotchas - especially in cluster ops across regions. Our own Stephen Blalock reveals a few insights of his first steps towards setting up and configuring a multi-region, causal cluster on Azure.
Friend of Graph Story and Neo4j Head of Dev Rel, Michael Hunger, shared a post on the first public release of the Neo4j graph algorithms library. It's about a 5 minute read and covers features provided with the new plugin, such as:
- Betweenness Centrality
- Strongly Connected Components
Michael discusses performance testing methods and implementation for the library. If you want to just jump into the docs, head over to Neo4j Graph Algorithms.
In a brief post from Thomson Reuters, Bob Bailey (Chief Information Architect), Dr. Tharindi Hapuarachchi (Technical Partnerships Manager), and Tim Baker (Global Head of Innovation) discuss insights and discoveries with graphs, specifically in the world of financial data.
If you haven't read it, it's new to you. From the article:
Thomson Reuters has been tracking movements of officers and directors of companies for over 30 years. Our Deals database spans a similar time period. By mapping organisations and people in both data sets to common permanent identifiers (PermIDs), a graph representation is formed exploring which executives are associated with which deals through time. Graphs like this can also be easily connected to other graphs as long as the graph databases share some common standards – typically around how entities (like people or companies) and relationships are represented.
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Found other awesome resources for graph dbs or Neo4j? Let us know!
Until next time (which is about two weeks from now),