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Graph Algorithms: Examples in Spark and Neo4j
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What is Neo4j?
Neo4j is a native graph database, built from the ground up to leverage not only data but also data relationships. Neo4j connects data as it’s stored, enabling queries never before imagined, at speeds never thought possible.
The Native Graph Advantage
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
With Neo4j, each data record, or node, stores direct pointers to all the nodes it’s connected to. Because Neo4j is designed around this simple, yet powerful optimization, it performs queries with complex connections orders of magnitude faster, and with more depth, than other databases.Learn More
Cypher — The Graph Query Language
With Neo4j, connections between data are stored – not computed at query time. Cypher is a powerful, graph-optimized query language that understands, and takes advantage of, these stored connections.
When trying to find patterns or insights within data, Cypher queries are often much simpler and easier to write than massive SQL JOINs. Since Neo4j doesn’t have tables, there are no JOINs to worry about. For comparison with SQL, here's a simple Cypher query matching all products in a category hierarchy:
Here's a similar query in SQL, which is longer and more complex. Unlike Cypher, where depth is unlimited, this SQL query selects just three levels of depth.
“Once we noticed that we were modeling transactions as a graph, we went out to look for a native graph database and we found that Neo4j was a great fit for this use case,” said Jorge Zaccaro, Software Engineer at Minka. How does Neo4j help you solve that problem? We describe our use case as ...Read More
Why You Need a Fully Managed Graph Data Platform Today’s developers and data practitioners face daunting Challenges: An urgent need for advanced analytics to inform business strategy Morasses of siloed data, subject to increasing regulation Digital initiatives slowed or even stalled Complex ...Read More
Welcome to this week's #GraphCast – our series featuring what you might have missed in Neo4j media from the past fortnight. Last time, our Managing Editor, Deb Cameron, featured a podcast with our very own Amy Hodler on real-world examples of knowledge graphs and why they're becoming such a big area of ...Read More