pygeoapi VS MongoDB

If you're just looking for a WFS (geojson/data) and not WMS/raster output, you might look at PyGeoAPI. (https://pygeoapi.io/) I haven't used it, but have looked at it a bit for a potential project to export geo data as an API.

Geoserver does fill a pretty big hole in the capabilities space -- it's pretty easy to get going with a bunch of layers and style them, but ultimately they're implementing a RDBMS in xml files, and it's a big, complicated, java system that's been one of the more troublesome portions of the stack (IME).

I would also like to share https://pygeoapi.io/ which relies on the new OGC API standard to share geospatial data.

python: https://pygeoapi.io - the whole project is ogcapi stuff

This may be a good start, without having to write much code. https://pygeoapi.io/

I believe pygeoapi https://pygeoapi.io/ is what you're looking for. It implements the OGC API suite of standards, and is in active development. I haven't used it yet, but hope to pretty soon within a Django project. If you do go with it I'd love to hear what you think.

https://pygeoapi.io/ link to the project. The devs are really active and helpful in Gitter of you ever have any issues

In order to publish the dataset using the OGC API Features standard, we need a software which implements the standard. In this tutorial we will use pygeoapi, which is a python server implementation, released under a FOSS (MIT) license. pygeoapi needs a backend to store the data. For that we will use the MongoDB document oriented database. In order to make deployment easier, the complete stack was virtualised into a set of docker containers, and orchestrated using docker-compose.

MongoDB

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.