Basic terminology and practices related to graph databases and graph modeling
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Re: Basic terminology and practices related to graph databases and graph modeling
#2Great tutorial on graph modeling! The author did an excellent job of explaining the basic terminology and practices related to graph databases and graph modeling. The tutorial is well-structured and easy to follow, making it an excellent resource for anyone looking to learn more about graph modeling. The author covers a wide range of use cases for graph databases, including social networks, fraud detection, network analysis, and supply chain management.
Re: Basic terminology and practices related to graph databases and graph modeling
#3Love this!
Re: Basic terminology and practices related to graph databases and graph modeling
#4I'm glad that you liked it. If you want to see all of these things in action check out Memgraph. You, can find our repo at https://github.com/memgraph/memgraph DISCLAIMER: I'm the co-founder and CTO.
Also, any feedback or suggestion will help us push more of content like this in the future!
Re: Basic terminology and practices related to graph databases and graph modeling
#5Great tutorial on graph modeling! The author did an excellent job of explaining the basic terminology and practices related to graph databases and graph modeling. The tutorial is well-structured and easy to follow, making it an excellent resource for anyone looking to learn more about graph modeling. The author covers a wide range of use cases for graph databases, including social networks, fraud detection, network a…
ChatGPT?
Re: Basic terminology and practices related to graph databases and graph modeling
#6This is an awesome introduction.
I wish there was a book/resource that explains when you should NOT use a graph DB( or any technology for that matter). And the pitfalls of using the wrong technology.
You have so many options for technology these days with so much overlapping capabilities it’s hard to decide which tech pick for which problem space.
Re: Basic terminology and practices related to graph databases and graph modeling
#7Great tutorial on graph modeling! The author did an excellent job of explaining the basic terminology and practices related to graph databases and graph modeling. The tutorial is well-structured and easy to follow, making it an excellent resource for anyone looking to learn more about graph modeling. The author covers a wide range of use cases for graph databases, including social networks, fraud detection, network a…
ChatGPT?
That was the first thing I thought.
Re: Basic terminology and practices related to graph databases and graph modeling
#8This is an awesome introduction. I wish there was a book/resource that explains when you should NOT use a graph DB( or any technology for that matter). And the pitfalls of using the wrong technology. You have so many options for technology these days with so much overlapping capabilities it’s hard to decide which tech pick for which problem space.
There is one section when not to use graphs in a blog post at https://memgraph.com/blog/graph-database-vs-relational-datab....
I guess it is like with any tool. You need to know the limitations. It is often better to use several tools. Each for the area where it performs the best. But then multiple tools can be pain to maintain.
EDIT: fixed typos
Re: Basic terminology and practices related to graph databases and graph modeling
#9This is an awesome introduction. I wish there was a book/resource that explains when you should NOT use a graph DB( or any technology for that matter). And the pitfalls of using the wrong technology. You have so many options for technology these days with so much overlapping capabilities it’s hard to decide which tech pick for which problem space.
> I wish there was a book/resource that explains when you should NOT use a graph DB( or any technology for that matter). And the pitfalls of using the wrong technology.
This is complicated by database companies, in particular, often marketing their products as suitable—or even best—for every situation, even when it's not true.
Graph databases are doing this now, but we saw the same thing with document-oriented databases like Mongo.
With graph databases I'd say the key things to look at are: data integrity / correctness guarantees (this one goes for any DB, really), and which graph operations and combos of operations they're best at. Nb that, depending on what exactly you're doing with a graphdb, your general data size & shape, and which one you're looking at, sometimes e.g. PostgreSQL actually outperforms them at graph-oriented operations.
[EDIT] General advice? Think about them if you've got a densely-connected, large graph and need to answer questions that mostly involve traversing the graph, but not fetching or inspecting much of that data as part of the queries, a graphDB might be a good idea—bearing in mind that using it as a supplement to an RDBMS is an option. Otherwise, it's less likely to be the right call (though it might be—various graph databases may perform very differently under the same workload, a query that runs like dogshit on one might do OK on another, usually this is due to their making different optimization trade-offs at the data structure level)