Haven't worked much with them before and mostly from a distance that they were already used in some corner of the project. This post makes me interested in learning more about them.
Thanks Saurabh! It was a completely new topic to me just a couple of months ago. If you work with loads of highly connected data, and you want to search for patterns, this is the way to go! I enjoyed your article!
As for the application, I like to think of anything where You don’t know in advance what data you want to store (including relations) because they are formed as you go.
Think of pharmaceutical companies where researching favourable compounds is a critical business task (Pfizer) or book retailers where you want to identify authors who publish AI written books. Also, fraudulent accounts in fintech apps. I’ll talk about these next week :)
The return value of the raw queries will depend on the query language you’re using. With Cypher - looks closest to the GQL standard - you can even return a JSON. Their native driver returns an array of records. Either way, when you use this on app level, it’s pretty much like talking to an ORM or the native Postgres driver.
I tried only JS libraries, but there should be drivers for GDBs in the language you use. In the JS world, the results and the interfaces these drivers expose are similar to other DB stuff, making adopting GDBs even faster.
Superb introduction to Graph DBs, Akos!
Haven't worked much with them before and mostly from a distance that they were already used in some corner of the project. This post makes me interested in learning more about them.
Also, thanks for the mention!
Thanks Saurabh! It was a completely new topic to me just a couple of months ago. If you work with loads of highly connected data, and you want to search for patterns, this is the way to go! I enjoyed your article!
graph databases are two types of RDF and priority graph,
they are no standard graph standard language like Ansi SQL, it will be Cypher in neo4j, amazon Neptune, Arango dB ,Apache gremlin, and tighergraph
these used in product recommendations, fraud detection and pattern matching
Yep, there will be more on these specific technologies in the next issue! Which one are you using in production?
I have never seen this, who's using it? What does the return look like when you run the query?
As for the application, I like to think of anything where You don’t know in advance what data you want to store (including relations) because they are formed as you go.
Think of pharmaceutical companies where researching favourable compounds is a critical business task (Pfizer) or book retailers where you want to identify authors who publish AI written books. Also, fraudulent accounts in fintech apps. I’ll talk about these next week :)
Okay, that's so interesting, looking forward to next week's issue
Thanks Jack! :)
The return value of the raw queries will depend on the query language you’re using. With Cypher - looks closest to the GQL standard - you can even return a JSON. Their native driver returns an array of records. Either way, when you use this on app level, it’s pretty much like talking to an ORM or the native Postgres driver.
Ah okay I have worked with JSON before, so I think I get the idea
I tried only JS libraries, but there should be drivers for GDBs in the language you use. In the JS world, the results and the interfaces these drivers expose are similar to other DB stuff, making adopting GDBs even faster.
Cool, thanks for explaining, sounds like an exciting development in the field