It happened to me that you actually construct a relational schema with MongoDB. It is not good. You do not use the modeling benefits of MongoDB, and you do not have the benefits of SQL. So I recommend taking it into MySQL. Since you think in a relational way, it is best you move to MySQL

Specifically, do you need non-normalized data? If not, MySQL is best. Otherwise, MongoDB is best. If you think non-relational, you do not need joins, and the problems with cascade disappear.

What is the best way to think? If you work in terms of whole tree of related object, then you think non-relational and non-normalized.

It makes no sense if you use MongoDB primarily as a relational database. As you scale MongoDB will be more expensive than SQL and as you said without having the advantages of "join" etc.

We use MongoDB in our company. It is useful for us, as we work with different types of devices and we love the functionality of being able to add fields whenever we have a new device type etc. Mongo also allows enables easy scaling and fault tolerance. However, you will have to learn how to manage it.

If you are already comfortable with SQL and don't need NoSQL, stick to SQL. At scale, it is cheaper than Mongo.

Look if you are comfortable with firebase you can go with it, after all, It's all about development and running your program bug-free and fast, but firebase is costly fo long run and if you are comfortable with that cost then I suggest you go with it.

Hi!

I’m not an expert, but I can tell you some things:

• Firebase is a great option for a very simple to implement, fast and reliable authentication method. Nonetheless, the free authentications are limited, so if you will potentially have millions of monthly authentications, it’s probably best to take the time to build it into your app directly.
• MySQL is great for simple tables where the data structures are not too complex, but it lacks some speed when you are trying to retrieve time data series. Also, I believe it’s a bit more difficult to distribute.
• MongoDB is great when your information is a bit more complex and you need very peculiar data structures, nested data, dynamic structures, etc. For me at least, it’s a bit more complex to master than MySQL, but the freedom it gives you is incredible. It also performs super fast, especially with time data series, and if I’m not wrong, it’s more scalable.

In general, almost all technologies have their good things, it’s just a matter of what you want to do and then choosing the right ones.

Doing User authentication (oauth) and session management by ourself is kind a challenging, so if possible use firebase itself since it provides these features out of the box.

MySQL is still more popular than MongoDB if you look at Google Trends. I've also added MariaDB, which is pretty much a copy from MySQL and its features, and PostgreSQL, which is also a popular relational database.

This is a very good article for comparing MySQL to MongoDB and which one you should use: MongoDB vs MySQL: A Comparative Study on Databases.

If you just want to learn and you have the time, I would opt for using both MySQL and MongoDB. For example using MySQL for most of the site content and MongoDB for saving log messages. As you get more and more logs you start to see the benefits from MongoDB's faster document fetching.

There's really not an awful lot of difference between the two, they have wildly different storage mechanisms but they each have their fairly similar benefits. If you want to learn something that might be a requisite skill for a job, I would also look at alternatives such as time based and column based systems like InfluxDB and the unbelievably fast and flexible ClickHouse. While they may seem like an unlikely fit for a personal bug tracker app, there's no reason not to use them. Since I got into InfluxDB people have been requesting it a lot and I'll be using ClickHouse for all large databases, probably forever. Expand your horizons beyond your competition's.

Just definitely don't use firebase. All of MongoDB, MySQL, MariaDB and PostGreSQL have a lot of community support and history.

Snowflake is a NoSQL database in the cloud, which also accepts SQL calls. Users can obtain an ODBC driver for SnowFlake, which would allow your Excel apps to write/read from the backend, locally.

Postgres is a better option to consider compared to MySQL. With respect to performance, postgres has an edge over MySQL. Don't use net/http for production. Read this https://medium.com/@nate510/don-t-use-go-s-default-http-client-4804cb19f779 I prefer gorilla/mux as it is simple and provides all the basic features. Other lib seems to be an overhead if you just need basic routing.

MySQL and Postgre both are great and awesome and great support, community, support. Whatever will be good. Postgree have some little advantages.

I recommend Elixir, even though I work in a fintech with Go, Elixir is a FP language so in my opinion the immutability is a important topic when working with money.

As i see it, you can use Snowflake as your data warehouse and marklogic as a data lake. You can add all your raw data to ML and curate it to a company data model to then supply this to Snowflake. You could try to implement the dw functionality on marklogic but it will just cost you alot of time. If you are using Aws version of Snowflake you can use ML spark connector to access the data. As an extra you can use the ML also as an Operational report system if you join it with a Reporting tool lie PowerBi. With extra apis you can also provide data to other systems with ML as source.

Postgres and MySQL are very similar, but Mongo has differences in terms of storage type and the CAP theorem. For your requirement, I prefer Postgres (or MySQL) over MongoDB. Mongo gives you no schema which is not always good. on the other hand, it is more common in NodeJS community, so you may find more articles about Node-Mongo stuff. I suggest to stay with RDBMS if possible.

This is a little about experience. Postgresql is fine. You can use either the related table structure or the json table structure.

We have a ready-made engine for the online exchange and marketplace. To customize it, you only need to know sql. Connecting any database is not a problem. https://falconspace.site/list/solutions

You can easily start with SQlite. Really easy to startup since it doesn't require you to install any additional software since is self-contained. It has interfaces in almost any language and also GUIs. Start learning SQL basics and simpler data models and structures. There are many tutorials, also available in the official website. From there you will easily migrate to another database. MySQL could be next, sonce it's easier to learn at first and has more resources available. PostgreSQL is less widespread, more challenging and has the fewer resorces, but once you have some experience with MySQL is really easy to learn as well. All these technologies are really widespread and used accross the industry so you won't make a wrong decision with any of these.

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

MySQL's very popular, easy to install, is also available as a managed service across most popular cloud offerings. The support/default tooling (such as MySQL Query Workbench) certainly is a little more baked than what you'll find for Postgres.

https://dev.mysql.com/downloads/workbench/

Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.