drizzle-orm VS Neo4j

Enter Drizzle, a lightweight typesafe ORM for TypeScript that comes with one promise: If you know SQL — you know Drizzle.

Drizzle ORM is a very popular TypeScript ORM that provides type safe access to your database, automated migrations, and a custom data model definition.

> being able to have a completely typesafe ORM such as Drizzle (https://orm.drizzle.team/) feels like a Rubicon moment, and touching anything else feels like a significant step backwards.

Alright, but there's nothing stopping you from having a completely typesafe ORM in python, is there?

Sure, there's isn't really one that everyone uses yet, but the python community tends to be a bit more cautious and slower to adopt big changes like that.

Drizzle [1] comes pretty close the last time I checked.

[1]: https://orm.drizzle.team

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

It's just SQL so you can take it out at any moment and move to any other DB provider. The package for working with Astro DB, @astrojs/db, includes Drizzle ORM so migration to a different provider should be relatively painless

Since Vercel's analytics pricing is a bit too expensive for my use case (where I hit the limit of 2,500 requests per month), and I didn't like using Google Analytics (not a big fan of Google), I decided to build my own analytics dashboard. Databases was something I didn't work with much before directly, so I decided to use an ORM, Drizzle, which is quite lightweight and easy to use.

do yourself a favor and ditch Prisma. It's a bloody mess of a project and codebase. I recommend https://github.com/drizzle-team/drizzle-orm to anyone that'll listen.

Neo4j

Neo4j: An ACID-compliant graph database with a high-performance distributed architecture. Ideal for complex relationship and pattern analysis in domains like social networks.

After spending millions fighting the committer of ONgDB who removed the commons clause from the AGPL branded license, it looks like the Free Software Foundation got involved and forced them to remove the commons clause or change the license to their own proprietary license.

https://github.com/neo4j/neo4j/commit/b6237ca4e31706b1efbd0f...

The GenAI Stack came about through a collaboration between Docker, Neo4j, LangChain, and Ollama. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running. The goal of the collaboration was to create a pre-built GenAI stack of best-in-class technologies that are well integrated, come with sample applications, and make it easy for developers to get up and running.

Neo4j (GraphQL)

NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and Cassandra.

this thread on this github issue could be useful.

Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.

For organizations and their applications that are designed to detect fraud, like International Consortium of Investigative Journalists, or try to improve customer experience via personalization, as in the case of Tourism Media, a NoSQL graph database like Neo4j is a good match. In these kinds of use cases, the quantity of data we're dealing with is enormous, and the pattern we're searching for in the data is often complex.

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.