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What is a graph database? Do you really need one, and if yes, how do you choose?
That’s what it all comes down to. This month’s edition of the Year of the Graph newsletter is special.
Apart from the usual hubbub, which is somewhat slower this time of year, this month the Year of the Graph offering expands to the Year of the Graph Report.
Other than that, this edition features 10+1 items to make for the extended time period it covers, and features mostly educational resources on using Gremlin, Cosmos DB, DSE Graph, on graph data modeling, and combining graphs with machine learning.
1. What is a graph database? Do you really need one, and if yes, how do you choose? I’ve been often asked those questions, and they are not the type that can be answered easily.
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2. All evidence points to the same direction — graph databases are poised for growth.
Markets & Markets projects a Compound Annual Growth Rate (CAGR) of 24.0% to reach a market size of USD 2,409.1 million by 2023.
3. If you want to get an overview of graph databases before getting to read the Year of the Graph Report, you can start with David Bechberger’s presentation. For everything you can’t find the answer to there, well, you know what to do.
4. Care for some expert advice on graph data modeling? Ted Wilmes gives his Graph Tips and Tricks that go beyond DSE Graph.
5. Need some more tips and tricks, this time of using Gremlin? What Jayanta Mondal talks about applies beyond Cosmos DB as well.
6. How about time traveling with graphs? Again, the techniques Daniel Larkin-York elaborates can be applied beyond ArangoDB
7. Wrapping up with tutorials, here is one on getting started with using GraphX on Spark to do graph analytics. One important point made here is that useful as it may be, this is NOT a graph database.
8. Let us not forget about query languages. With the call for a standardized query language for labeled property graphs still open in the community, TigerGraph’s Mingxi Wu weighs in with his opinion on what matters for graph query languages.
9. DeepMind’s Marta Garnelo has been working on combining symbolic methods for AI with machine learning. Although the former have been overshadowed by the latter, Garnelo argues they should be able to complement each other, and sees a role for RDF in this.
10. People in Octavian have also been working on the intersection of graph and machine learning, and they just released a dataset that can be used for question answering.
11. Here is how the DIG team built their Domain Insight Graph to help counter trafficking using RDF:
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Originally published at linkeddataorchestration.com on July 19, 2018.
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