#neo4j
Diesmal ein Protagonist der NoSQL-Bewegung. Im 5. Teil seiner Reihe über Architektur-Ikonen der #Softwareentwicklung im IT Spektrum portraitiert @StefanZoerner die Graph-Datenbank @neo4j. Was waren die Ziele, was die zentralen Entwurfsentscheidungen? Neugierig? 👉 Dann lest den Beitrag online bei uns im Blog: https://www.embarc.de/architektur-portraet-neo4j/

Finally got the error fixed, added locations to the graph and added images of the connections to show the nodes and relationships to my Final Fantasy Tactics to Neo4j project. Check it out if you want.

Hello everyone!
In today's post, we'll discuss the data modeling and schema design for our Golang project, which focuses on analyzing a marketplace's purchase data. This analytical application will help us understand the relationships between sellers, products, and customers, as well as feedback provided by customers on their purchases. The marketplace operates in various locations, including countries and states.
Let's dive into the details of the data schema:
Node types:
Location: Represents a country or a state.
Seller: Contains attributes such as name, locations where the seller offers products, and feedback scores.
Product: Includes attributes like ID, price, and a relationship to its seller.
Customer: Has attributes like ID, location, and represents clients who ordered products and optionally provided feedback on their purchases.
Relationships:
Seller to Location: A many-to-many relationship, indicating the locations where a seller offers their products.
Product to Seller: A many-to-one relationship, connecting each product to its respective seller.
Customer to Product: A many-to-many relationship, representing the products purchased by customers.
Customer to Product (Feedback): A many-to-many relationship, where customers can provide feedback on the products they purchased.
With these node types and relationships in place, we can efficiently model our analytical application about marketplace purchases in a graph database. In the following posts, we'll explore different graph databases and evaluate how well they can handle our data model, in terms of installation, code samples, and performance.
Stay tuned for our next post, where we'll dive into EdgeDB and explore its potential for our Golang project. See you there!
```
classDiagram
Location "1" -- "many" Seller : Offers Products In
Seller "1" -- "many" Product : Sells
Customer "many" -- "many" Product : Purchased
Customer "many" -- "many" Product : Gave Feedback On
class Location {
+ID: Integer
+Name: String
}
class Seller {
+ID: Integer
+Name: String
+FeedbackScore: Float
}
class Product {
+ID: Integer
+Price: Float
}
class Customer {
+ID: Integer
+Location: String
}
Purchased --|> Customer
Purchased --|> Product
class Purchased {
+Count: Integer
}
GaveFeedbackOn --|> Customer
GaveFeedbackOn --|> Product
class GaveFeedbackOn {
+Score: Float
}
```
Copy the code above and paste it into the Mermaid Live Editor (https://mermaid-js.github.io/mermaid-live-editor/) or any other Mermaid-compatible diagramming tool to generate the updated schema diagram
#golang #graph #data_modeling #schema_design #edgedb #neo4j #dgraph
Cool talk by Marcus Tedesco at #Pycon #berlin
"#Neo4j graph databases for climate policy"
https://2023.pycon.de/program/YTHXML/
There is also the EU project ARSINOE for UN-Sustainable Development Goals SDG
https://www.frontiersin.org/articles/10.3389/fenvs.2022.1003599/full
https://www.youtube.com/watch?v=-6lr-GtsoLk
My Goodreads repository now contains a bunch of examples how to interact with CSV data https://github.com/michael-simons/goodreads#interacting-with-the-csv-file, including #sqlite #duckdb #neo4j and #xsv
I’ll be part of the #Bucharest #Techweek in Romania as part of their #Java summit https://www.techweek.ro/java-summit and I’ll discuss an old topic: #database #refactorings and #migrations and why these things make a lot of sense for a #Graph database like #neo4j that is not so schemaless like you would think.
TIL the `guix graph` command has a backend that outputs [cypher queries](https://neo4j.com/docs/cypher-manual/current/) (for use with [neo4j](https://neo4j.com/))
Why can't databases just manage themselves? :blobfoxsad:
Und hier jetzt noch der Workshop-Bericht zum Workshop "Neo4J für Korrespondenzdaten" mit @FKlemstein am letzten Montag:
What a nice message to wake up to. My #Neo4j database #migration and #refactoring toolkit has been featured on #Githubs release radar. https://github.blog/2023-02-08-release-radar-dec-2022-jan-2023/
An #opensource consortium that includes #Google plans to release a deployable beta of the #GUAC project this month, a possible milestone for #cloudnative #SBOM. https://www.techtarget.com/searchitoperations/news/365532041/SBOM-graph-database-aims-to-be-cloud-security-secret-sauce
ETA: As my colleague @robwright astutely noted on the birdsite, this has some potentially far-reaching implications given the #WhiteHouse #NationalCybersecurityStrategy announcement...
#knowledgegraph #graphql #graphdatabase #Neo4j #cloudnativesecurity #cncf #OSS #opensourcesecurity #softwaresupplychain
Neo4j-Migrations 2.1.2 has been released: https://github.com/michael-simons/neo4j-migrations/releases/tag/2.1.2
This is merely a bugfix release.
Neo4j-Migrations is a database #migration tooling for #Neo4j.
Today we released Cypher-DSL 2023.1.0, the first version that does include a bit of semantic analysis of the #Cypher statements being built or parsed with its parser module.
This will allow for additional verification, checks and possible mappers in Spring Data #Neo4j and other modules.
Big shout-out to @lukaseder for feedback and input and ofc helping us getting a #sql2cypher PoC to work.
Links in the release notes:
https://github.com/neo4j-contrib/cypher-dsl/releases/tag/2023.1.0
Heute fand an der @stabihh im Referat der erste Workshop der Reihe "Digital Humanities - Wie geht das?" statt, bei dem @FKlemstein am Beispiel von Korrespondenzdaten aus der #DehmelDigital Edition in #Neo4J eingeführt hat. Ergänzend dazu gab es fantastisches Wetter und einen tollen Blick über #Hamburg. Einen Workshopbericht gibt es demnächst auf dh3.hypotheses.net.
Next university project: building a graph database of #Rust dependencies using the #CratesIO database dumps <https://crates.io/data-access> and #Neo4J!
Vertical Knowledge is hiring Graph Data Scientist/Ontology Specialist
🔧 #c #neo4j #sql
🌎 Chagrin Falls, United States
⏰ Full-time
💰 $115k - $250k (Estimate)
🏢 Vertical Knowledge
Job details https://jobsfordevelopers.com/jobs/graph-data-scientist-ontology-specialist-at-vk-ai-jan-6-2023-80f877?utm_source=mastodon.world&ref=mastodon.world
#jobalert #jobsearch #hiring
Moin #DigitalHumanities Bubble,
es gibt noch _einen_ freien Platz im #Graphen Workshop zu #Neo4J für Korrespondenzdaten mit @FKlemstein, am 27.02. von 9-13 Uhr in Hamburg an der @stabihh
Wer noch teilnehmen will, findet hier weitere Infos: https://blog.sub.uni-hamburg.de/?p=34979
Neo4j is hiring Senior Sales Engineer
🔧 #dotnet #sql #java #javascript #python #docker #kubernetes #neo4j #nosql #seniorengineer
🌎 New York City, United States
⏰ Full-time
💰 $150k - $256k (Estimate)
🏢 Neo4j
Job details https://jobsfordevelopers.com/jobs/senior-sales-engineer-at-neo4j-com-jan-30-2020-caaef0?utm_source=mastodon.world&ref=mastodon.world
#jobalert #jobsearch #hiring
Populating the graph database on ultrahdzone.com takes some time. #neo4j doesn't like parallel requests (some of the queries are quite large), so the most sustainable path was a recursive script that creates all the metadata and relationships for each 4K disc.
The short term gain is interesting ways to find new 4K Blu-ray based on relational data, long-time gain is finding titles like another or are subtitled in a particular language, etc. Graph DB FTW! #programming
#Mastodon instance #admin folks may be interested in this: This morning, my #Neo4j colleagues Michael Hunger and Alexander Erdl demonstrated, in this webinar ( https://www.youtube.com/watch?v=14l01K18Ako ), how #graphdatabase technology may be put to use immediately in conducting visual analyses and relationship-based queries of the publicly available social transaction data available from docs.mastodon.org. See how users find other users through messages, and conduct Cypher queries on the results.
I'm hoping one of the things I can accomplish here on Mastodon, that I couldn't possibly on Tw****r, is frank discussions with knowledgeable people about what my colleagues at #Neo4j are doing with problem-solving at scale, using #graphdatabase - for example, finding cures to #disease .
I recently published this article
( https://neo4j.com/developer-blog/neo4j-graph-databases-for-beginners-2023-edition-chapter-1-relationships/ ) that should explain this craft to intelligent people like yourself.
It's really only a matter of time before one of the Mastodon instances bridges to a #neo4j Enterprise install and gives you all the features you could possibly want:
- Link prediction (aka, which people should I follow that I haven't or which tweets should I see that I haven't?)
- Regexp (and more) searches
- "Today's most popular tweets in your interests/followers"
etc.
Probably not more than a good couple weeks to implement this, tbh.