Neo4j unifies organizations across departments and across teams, both technical and non-technical, enabling a greater level of understanding and clarity in communication than previously possible. A Neo4j graph model is whiteboard friendly and allows everyone from business to engineering groups to speak the same language of connections. Communicating in contextually relevant connections that bring together business concepts reduces the potential for misunderstandings that cause delays and rework later.
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Enterprises today are amassing data at a faster rate than ever before and largely this data flows into a data warehouse or data lake or just individual databases where it sits. With enterprises struggling to leverage it in a holistic and meaningful way for their business, the appeal of “big data” is waning. So how do enterprises begin moving beyond big data?
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At GraphConnect at the end of April the Neo4j team announced the release of Neo4j 3.0. We had the opportunity to celebrate this release at The Honest Company last night with the Graph Database LA Meetup group where I shared many of these highlights from the official Neo4j announcement. The first release in the 3.x series ushers in a new era of scalable yet reliable graph database technology with, this version of Neo4j based on a completely redesigned architecture that offers enhanced developer productivity, and varying deployment options at a massive scale.
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Data migration is one of the necessary evils involved with keeping a database aligned with the evolving needs of the business and applications using it. With the increasing demand for enterprises of all sizes to iterate more quickly and drive change from within the data migration conversation becomes much more frequent. Data migration procedures are something that can take a very long time or not even be feasible depending on the size and structure of the data in a database.
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Many enterprises today build their business around research that involves piecing together meaningful data from the public domain for their customers. When trying to connect data across a domain in a meaningful way building around a graph database is a great tool because it models very well exactly how the business analysts at these research organizations are piecing together the real-world data they are finding during their research.
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