SqlKata Query Builder VS MongoDB

SqlKata is your friend.

Don't know if can be a good pick for the no-ORM requirement but I would take a look at SqlKata which is a nice query builder + execution engine, built on top of Dapper

Not really an ORM. But I have been enjoying SqlKata recently. Works with Dapper but helps reduce SQL strings and makes things like pagination really easy. Also nice for dynamic filters.

That is basically the description of an object mapper, with all the guarantees of an object mapper :). It seems if you actually use the query builder as such, no guarantees exist.

I'm pretty picky regarding query builders and ORM's, to the extent of having written several of them over the years, in different languages (both dynamic and strong typed, unfortunately closed-source). I'm a strong advocate of schema-first design, and usually a query builder will allow you to design your queries explicitly, but having some internal behaviors (such as string concatenation, identifier quoting and automatic in-order separation of parameters and values to be bound) taken care of. As good examples of this, I'd mention golang's goqu (https://github.com/doug-martin/goqu) and - to some extent - C# SqlKata (https://sqlkata.com/). Following my frustrations with Python ORMs, I built my own toy project, sort-of-in-beta, called rickdb (https://github.com/oddbit-project/rick_db).

Less about the exact syntax and more about the tool, for example: https://github.com/sqlkata/querybuilder. I just chose that since it was on top of a search but the idea is the same. Your code generates raw SQL, so it's 100% interchangeable with writing SQL yourself however the builder library deals with the syntax, proper ordering, quoting, full attribute names, etc. Some such libraries even let you define your schema in code to make your SQL generation type safe.

First, visit MongoDB Atlas and create an account, or sign in if you already have one. This article will guide you through the process of creating a MongoDB account. You should be redirected to your dashboard once you have completed the process. Locate the Connect button and click it.

On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra.

I built each API with Node.js, Express, and Docker. Services connected to a NoSQL MongoDB database.

In edge computing, managing time series blob data efficiently is critical for performance-sensitive applications. This blog post will compare ReductStore, a specialized time series database for unstructured data, and MongoDB, a widely-used NoSQL database.

MongoDB to store our data as documents, close to JS objects

MongoDB: Known for its ease of development and strong community support, MongoDB is effective in scenarios where flexible schema and rapid iteration are more critical than strict ACID compliance.

MongoDB

Ensure you have MongoDB installed for data storage. You can download MongoDB Community Server from MongoDB's official website or use the cloud cluster.

We will be using MongoDB as a database on both the Auth microservice and notifications microservice, sign up for a MongoDB Atlas account here incase you donot have one and donot have its desktop application(mongodb campass) installed and would like to use mongodb atlas. This cloud-based database service offers a free tier and simplifies the process of managing MongoDB databases.

Head over to MongoDB and create an account or login to grab your connection string.